Modeling and Cost Analysis of Global Logistics and Manufacturing System
by
Te-San Liao
B. S., Naval Architecture Engineering
National Taiwan University, 1991
Submitted to the Department of Ocean Engineering
in Partial Fulfillment of the Requirements for the Degrees of
Master of Science in Operations Research
and
Master of Science in Ocean Systems Management
at the
Massachusetts Institute of Technology
June, 1997
C 1997 Massachusetts Institute of Technology
All rights reserved
.............
........................ .........
Department of Ocean Engineering
Signature of Author ......
S
Certified by .........................
May 20 t , 1997
•
..
• ... ..... .on
' . . . ........
Don 'B. Rosenfield
Director, Leaders for Manufacturing Fellows Program
S _
Thesis Supervisor
Certified by ...........................
t.;+16.
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........
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..... ..............
Henry S. Marcus
Professor, Ocean Systems
Thesis Supervisor
Accepted by ......................................................... .
... ............
:- s
sThomas
L. Magnanti
or,_j.. , :t"ov
George Eastman Professor of Management Science
JUL 15 1997
LIBRARIES
Eng. Professor of Electrical Engineering and Computer Science
.,
Accepted by ..................................
Codirector, OpprationiS Research Center
............... . . . . . . . . . . . .
...
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Professor, J. Kim Vandiver
Chairman, Departmental Committee on Graduate Studies
Modeling and Cost Analysis of Global Logistics and Manufacturing System
by
Te-San Liao
Submitted to the Department of Ocean Engineering
on May 21st, 1997 in Partial Fulfillment of the Requirements for the Degrees of
Master of Science in Ocean Systems Management
and
Master of Science in Operations Research
ABSTRACT
Three major transportation modes, sea shipping, railroad shipping, and trucking, are
explored in terms of the factors that affect pricing policy. This thesis explores the factors
and the pricing policy for each transportation mode, develops the cost model of each
mode, and uses their cost models to examine the accuracy of an approximate total cost
model. These cost models are also applied in estimating total distribution and operational
costs in automobile industries. The effects of number of plants and their locations on
total operational costs in global markets are inspected.
Thesis Supervisor : Donald B., Rosenfield
Title : Director, Leaders for Manufacturing Fellows Program
Thesis Supervisor : Henry S., Marcus
Title : Professor, Ocean Systems
Acknowledgments
Supports for this thesis came in many forms, technical, emotional and financial.
Without undue exaggeration, the greatest contributor in all three areas was my advisor,
Donald Rosenfield. He spent so much time and effort, especially in the late stages of the
thesis, to read my thesis and give me advice. The entire process has been a valuable
learning experience for me. Thanks for all his patience, advice and generous financial
support during my thesis preparation.
I would like to thank Professor Henry Marcus who served as academic advisor
and thesis reader. Thanks for his help and advice during my study at MIT.
I would like to thank Chia-Ming and Yu-Hsuan, my best friends at MIT. Thanks
for their supports and friendship over years.
To my friends at MIT and in Boston, being with them made my life at MIT full of
fun and easier.
Sincerest thanks to my girlfriend, Jasmine Hsing Chao, for her love,
encouragement, and companionship.
Finally, I would like to express my deepest appreciation to my family, my parents
and sisters, for everything.
Contents
Page
5
List of Tables and Figures
Chapter 1
Introduction
Chapter 2
Transportation Costs and Models
Chapter 3
7
11
2.1
Introduction and Basic Theory for Pricing ........................ 11
2.2
Sea Shipping .......................................
2.3
Railroad Shipping ....................................................
25
2.4
Trucking ....................................................
29
......... 14
Total Cost Model of Global System and Case Analysis
3.1
36
Case I: Comparison of the Results of the Geographic Distance
Model and the U.S. Automobile Market ............................. 39
3.2
Case II: Analysis for the projected demand in the world
from 1997 to 2002 ..................................................... 44
3.3
The Limitations of the Application of the Geographic Distance
M odel ......................................
Chapter 4
....
................ 51
53
Summary and Conclusion
Bibliography
Appendix
58
Appendix I ...................................... .................
Appendix II ......................................
.....
58
................. 65
List of Tables and Figures
Tables
Chapter 2
2.2.1
Factors of Determining Freight Structure
15
2.2.2
Detailed Cost Per 20 Feet Container (Per TEU) of 2,800 TEU ships
16
2.2.3
Layout of 'Article Matrix' of a Cost-Based Tariff of Freight Rates
19
2.2.4
Costs per Ship (2,800 TEU) on Annual Basis in Cross-Pacific Trade
22
2.2.5
Costs per Ship (1,600 TEU) on Annual Basis in Cross-Atlantic Trade
24
2.3.1
U.S. Railroad Revenue Per Carload
25
2.3.2
Railroad Freight Rate Data for 1988
27
2.4.1
Weighted Average Used in the Trucking Cost Function
33
2.4.2
Coefficient Estimates for Cost Functions of Official, South-West
and Interregional Carriers
2.4.3
Elasticity of Costs with Respect to Output and Operating Characteristics
at the Point of Approximation, Carrier of Specialized Commodities
Chapter 3
3.1.1
The Results of Geographic Distance Model and Direct Calculation
3.1.2
Total Shipping Costs for One Plant System
with Different Plant Locations
3.2.1
Annual sales of Ford Taurus in major regions in the world
3.2.2
Demand Distribution in Western Europe
3.2.3
Demand Distribution in the Far East
3.2.4
Plant Locations in the Systems with Different Numbers of Plants
3.2.5
Costs for 1-Plant & 2-Plant System
3.2.6
Costs for Two Alternatives in 3-Plant System
Chapter 4
4.1
33
Change of Plants, Fixed Production Costs, and Distribution Costs
35
Appendix
A.1.1
Transportation Cost in the U.S. with One Plant
A.1.2
Transportation Cost in the U.S. with Two Plants
A.1.3
Transportation Cost in the U.S. with Three Plants
A.1.4
Transportation Cost for Alternative Plant Locations in the U.S.
in One Plant Case
A.2.1
Distribution Costs in Global Systems with 1 Plant
A.2.2
Distribution Costs in Global Systems with 2 Plants
A.2.3
Distribution Costs in Global Systems with 3 Plants (Taiwan)
A.2.4
Distribution Costs in Global Systems with 3 Plants (Salt Lake City)
Figure
3.1.1
Geographical Distribution of Projected Demand
for Ford Taurus in the U.S
Chapter 1 Introduction
Transportation and information technology have developed rapidly in past years.
It is now easier for companies to respond to demands for their products and to distribute
their products to their clients. In addition, competitive pressures are requiring more
companies to go global. In the past several years, many corporations have substantially
increased the magnitude of global operations, including both manufacturing and
distribution.
When a company plans to set up a global manufacturing and distribution network,
it must consider two major alternative systems: (1) a centralized production system building a big plant to finish the production process, and then using its distribution and
logistics channels to ship its products to its clients; (2) a decentralized production system
- building several plants in different places, and using the products of these plants to meet
the demand in the local market or nearby markets. For example, if the company chooses
the decentralized system, it may build one plant in the U.S. to meet the demands in the
U.S. market, and another plant in Western Europe to meet the demands in Western
Europe, Eastern Europe, and Africa. Both of these systems have their own advantages
and disadvantages. According to its situation, a company will carefully choose between
these two alternatives in order to reduce total operational costs and increase profits.
In these two systems, the number and the locations of factories are the key factors
in determining total operational costs. There are many factors that will affect decisions
about the number of plants and their locations: transportation costs, manufacturing costs,
taxes and duties, local issues and local restrictions, exchange-rate uncertainty, etc. These
factors make a global system more complex and difficult to be analyzed.
Global operations have become more important in recent years. For this reason,
several authors have explored this area. Arntzen, Brown, Harrison, and Trafton (1995)
used global supply chain model to analyze the manufacturing and distribution strategy of
Digital Equipment Corporation'. They modeled the important issues in global system,
such as, demand, production, inventory, shipping, duty drawback and duty relief.
Huchzermeier and Cohen (1996) discussed the issues of valuing operational flexibility
under exchange rate risk in global system 2. Rosenfield (1996) analyzed multi-regional
operations and used stochastics models to simulate exchange rate uncertainty3 .
Rosenfield used three major costs to compose the majority of total operational
costs: production costs, distribution and handling costs, and taxes. For most products,
these costs consist more than 80% of operational costs. They used a geographic distance
model to model distribution and handling costs in local system4 . This model states that
the average distance over which the products are shipped is proportional to the square
root of the area served by the plant. Total transportation costs are also proportional to
demand. The total transportation costs can be expressed as:
Total Transportation Costs = A * D * r12
Here, A is a constant coefficient, D is the total demand in the whole market, and r is the
average area served by each facility. Because
r=R/M
Therefore,
Total Transportation Costs = A * D * r
2=A
* D * (R / M )12
= C * D * M(112)
R is the total market area served. M is number of total plants in the whole market. C is a
constant and equal to A * R 12 .
SArntzen, Bruce C., Brown, Gerald G., Harrison, Terry P., and Trafton, Linda L. (1995), Global Supply
Chain management at DigitalEquipment Corporation,Interfaces 25, Jan.-Feb., 1995
2Huchzermeier, Arnd, and Cohen, Morris A. (1996), Valuing OperationalFlexibility UnderExchange Rate
Risk, OperationsResearch, Vol. 44, No. 1, Jan. Feb., 1996
3Donald B. Rosenfield (1996), Global and Variable Cost ManufacturingSystems, European Journal of
Operational Research, 1996
4Magee, John F., Copacino, William C., and Rosenfield, Donald B. (1985), Modern Logistics
Management, Chapter 10, John Wiley & Sons, N.Y
One of the purposes of this thesis is to test such simple approaches in estimating
transportation costs.
In global systems, there are four major transportation modes: sea shipping,
railroad shipping, trucking and air shipping. The costs of these four models constitute
total transportation costs. Every shipping mode has its specific characteristics and
supply-and-demand relationship. Carriers of different shipping modes will have their
specific considerations and procedures to determine their freight rates. Even though there
are common factors needed to be considered in all shipping modes, these factors will
have different weights in different modes. For example, distance is a critical factor in
determining the freight rate of trucking. However, because facility, capacity and
accessibility of seaports are very important in determining sea shipping costs, compared
to these factors, distance becomes less important for sea shipping. Therefore, each
shipping mode should have its unique model for freight rates.
On the other hand, for different products, transportation costs are responsible for
different portion of total costs. In some cases, transportation costs account for just 10%
or less of total costs. However, in some other cases, the portion may be more than half of
the total costs, for instance, raw material transportation. Therefore, applying
oversimplified models to estimate total transportation will induce a different scale of
errors in total operational costs.
In the automobile industry, because many components are required to assemble a
car, and it is difficult to produce all components at a same plant, automobile companies
have to ship some components from other plants or component suppliers. Transportation
and shipping costs become more important in the automobile industry than those in other
industries. We thus explore the automobile industry because first, it is one of the largest
industries in the world, and second, transportation costs are critical in the automobile
industry.
The objectives of this thesis are:
1. To realize the characteristics and important factors of different transportation modes,
with a major product of the automobile industry.
2. To develop cost models for different transportation modes.
3. To check the accuracy of simplified models such as the Geographic Distance Model
by using the freight rate models of these transportation modes.
4. To analyze and estimate global operational costs in the automobile industry.
Aircraft cannot carry very heavy cargoes, because of its characteristics. For this
reason, automobile companies rarely use this mode to ship their products. Therefore, the
mode of air shipping is not discussed in this thesis.
In Chapter Two, the general pricing theory in shipping industries is introduced.
Total transportation costs are divided into three parts: sea shipping cost, railroad shipping
cost and trucking cost. For each mode cost, I will analyze its specific characteristics, find
the important factors in determining its freight rate, and develop its individual pricing
model.
Chapter Three includes two major parts. In the first part, the cost models of the
three modes are combined together as the total transportation cost model. This model and
the Geographic Distance Model are used to analyze the U.S. automobile market. By
comparing their results, the accuracy of the Geographic Distance Model is examined. In
the second part, the total transportation cost model is associated into total operational cost
model. The case of a global automobile market is analyzed by using the total operational
cost model. Chapter Four concludes the thesis.
Chapter 2 Transportation Costs and Models
2.1
Introduction and Basic Theory for Pricing
Because of the deregulation in most major modes of transportation, it is difficult
to realize the pricing behavior of them by a regulated formula. The reasons are: first,
there are too many factors involving in determining freight rates. Some of the factors are
related to shippers, such as shipped commodity characteristics and total shipped amount.
Some factors are related to carriers, such as total capacity of the individual carrier and all
carriers. There are also some other factors that are unrelated to both of them, such as
shipping season. Second, for different transportation modes, there are different factors
resulting in total operation cost for carriers. Even when different modes have common
determinants, these determinants have different weights in determining freight rates. For
example, fixed cost or equipment cost is not so important for trucking, but it covers a big
part for sea-shipping. Therefore, economists spent a lot of time and energy to determine a
proper policy to determine freight rates.
The theoretical way to approach pricing strategies for different modes of
transportation is through a well-known theorem in economics. It states that a competitive
equilibrium is Pareto optimal and therefore efficient in the sense that no industry can
increase its output without decreasing that of another, and no individual's welfare can
increase without decreasing that of another. Economists analyzing the efficiency of the
surface freight industry have generally focused on competitive equilibrium as the norm.'
Economists also suggest that it is a reasonable pricing policy for carriers to charge
shippers the marginal shipping cost in order to get maximum profit for the whole
commodity.
1 See Meyer et al. (1959) and Moore (1975)
In following two examples, one in the competitive market and the other one in
joint profit maximizing market, we can see what this economic theorem means, how it
works, and why it is reasonable for carriers to charge marginal cost.
In a competitive market, if price is less than marginal cost, price should be
required to increase and then be equal to marginal cost, because of over-utilization of
resources and increasing demand in that sector. If price is greater than marginal cost, then
through the function of supply-and-demand in economic market, then the normative price
should be forced to decrease to attract demand and then be equal to marginal cost. Here,
demand is estimated on an industry level and is postulated to be a function of rates, trip
attributes, non-transportation output, etc., and cost is estimated on a firm level and is
postulated to be a function of output, factor prices, and trip characteristics.
On the other hand, in joint profit maximizing market, if one firm, through
ownership, or a group of firms, through collusion, were to gain monopoly power in an
industry, it has enough power to affect market price. Neoclassical economic theory states
that the industry will produce where marginal cost equals marginal revenue in order to
maximize profits for the whole community. It is obvious that the firm will not allow a
market price less than marginal cost, otherwise it will lose money. Moreover, the firm
will tend to set the price as high as it can in order to get more profit. However, when the
market price is greater than marginal cost, even though the firm can get more profit, there
is a "deadweight loss" in the whole community. "Deadweight loss" is not characteristics
of customers or the firm, but lost in the whole system. From the viewpoint of resource
efficiency for the whole system, because a resource is used and no benefit is produced,
this is not an efficiently economic system. Therefore, in an industry that produces many
outputs, profit will be maximized where marginal cost equals marginal revenue for each
commodity.
From the previous two examples, apparently, charging marginal cost is reasonable
and efficient for carriers, shippers and the whole system. Therefore, the policy of
charging marginal shipping cost is generally accepted and used as the basic rule in
current freight rate structure for most transportation modes. I also use a marginal cost
model to approach freight rate in this thesis.
In addition to economic forces, federal transportation policy is another important
determinant of transportation activity. This includes not only the equilibrium quantity of
service, but also its quality and cost. It is fairly easy to visualize how transportation
regulation, through restrictions on entry, rates and routes, can affect the equilibrium
service and costs of a particular mode. What should be realized is that because the
various modes are in competition, the relative service levels of all modes have been
affected.
It should be noted that there are many modes competing for freight, for instance,
railroad, trucking, barge, pipeline and air freight. However, tractability calls for the
problem to be segmented. Pipelines mainly carry natural gas and petroleum products in
large volumes. Barge competes mainly with rail for bulk commodities. Air freight makes
up a very small share of the transportation market. Trucking is both a substitute and
complement for railroad transportation. Full truckload truck transportation competes
directly with rail, and less than truckload lot truck transportation offers a service
complementary to that of rail.
In this thesis, my focused commodity is motor vehicle. Because of the high freight
rate and the capacity constraint of air shipping, most of these commodities are not
shipped through air. So I will not discuss the freight structure of air shipping.
In this chapter, I focus on the pricing behavior of three shipping modes: sea
shipping (Section II), railroad shipping (Section 111), and trucking (Section IV). In each
section, I will generally discuss the important factors in determining marginal cost and
freight rate of this mode, and the effect of these factors on the pricing behavior. Finally, I
will use the existing data to develop an individual mathematical model for this mode.
2.2
Sea Shipping
The most conspicuous feature of the sea-shipping tariff has been its size:
practically every one of hundreds or more different articles are separately identified so
that each can, in principle, carry an individual freight rate. When, as is common, a
separate rate is quoted for each specific commodity, one speaks about 'commodity rates'.
A single tariff may include several thousands of commodity rates. In some tariffs, freight
rates are divided into ten to twenty classes, and the commodities are assigned to different
classes. These quotations also apply to containerized cargo. For a less-than-full
container load, commodity rates as published for conventional cargo normally apply. For
a full container load, a simplified tariff which may include twenty or thirty commodity
classes normally used.
Based on the principle of economic theory described in Section 2.1, the freight
rate structure can be approached by marginal costs.
There are many factors that participate in shaping the level and structure of freight
rates, and which makes it very difficult to compare individual freight rates. For example,
the following rather exhaustive list (Table 2.2.1) of twenty-seven factors was suggested
by the US delegates in the Inter-American Maritime Conference in 1941.
Table 2.2.1
Factors of Determining Freight Structure
(Source: Inter-American Maritime Conference, Report of Delegates of the United States, Washington,
Government printing Office, 1941)
1. Character of cargo
23. Port regulations
2. Volume of cargo
24. Port charges and dues
3. Availability of cargo
25. Canal tolls
4. Susceptibility to damage
26. Port location
5. Susceptibility to pilferage
27. Possibility of securing return cargo
6. Value of goods
7. Packing
8. Direct cost of operation
9. Distance
10. Cost of handling
11. Lighterage
12. Special deliveries or devices
13. Fixed charges
14. Insurance
15. Package
16. Stowage
17. Heavy lifts
18. Extra length
19. Goods from other sources of supply
20. Goods via competitive gateways
21. Competition from other carriers
22. Port facilities
For costing purposes, the marginal cost of the kth cargo can conveniently be
divided into three parts: (1) Fixed cost component, (2) Direct cargo cost component, (3)
Indirect cost component. We can see more detailed components in these three groups and
their percentage of total cost in Table 2.2.2
Table 2.2.2
Detailed Cost Per 20 Feet Container (Per TEU)
of 2,800 container ships
(Source: Container Market Profitability to 1997)
1. Fixed Costs
$ Per Teu
Bunkers
61
3.5
Ports
62
3.6
Capital
168
9.7
Operating
133
7.7
Administration
281
16.2
Subtotal
707
40.8
Terminals
286
16.5
Transport
470
27.1
Depots
7
0.4
Refrigeration
7
0.4
Subtotal
769
44.4
Empty Containers
85
4.9
Equipment Provision
88
5.1
Maintain & Repair
68
3.9
Cargo Insurance
16
0.9
Subtotal
256
14.8
1733
100
2. Direct costs
3. Indirect Costs
Total Costs
Explanation:
Fixed costs: the cost irrelative to shipping quantity
* Bunkers - Fuel for the ship's engines.
* Ports - Pilotage, towage, dockage fees, port dues, etc.
* Capital - Payments toward equity in the vessel, including interest charges.
* Operating - Stores and lubes, ship repairs and maintenance, insurance and managing
the ship
* Administration - Managing the movement of cargo through the service network
Direct Costs: the cost relative to shipping quantity and induced in the shipping process
directly
* Terminals - Moving containers on and off the vessel, including terminal gate charges,
crane usage, transfers, removal of hatch covers and all other interminal cargo
expenses
* Transport - Cargo movement by rail, truck or barge from the port to an inland
destination
* Depots - Costs for consolidating cargo into full container loads (stuffing/stripping) at
container freight stations
* Refrigeration - Cost for provision of refrigeration facilities and monitoring the
temperature of frozen cargo
Indirect Costs: the cost relative to shipping quantity and induced indirectly
* Empty Containers - Cost for restowage, transportation and loading of empties. Does
not include opportunity cost of not carrying full containers
* Equipment Provision - Cost for containers and trailers, includes both leasing and
purchasing costs
* Maintenance & Repair - Costs for maintaining containers and trailers
* Cargo Insurance - Covers the cargo on both the land and sea portions of the trip
The investigation of the structure of break-bulk cargo handling costs 2 indicates
that a very substantial proportion of the variations in both the direct and indirect handling
costs are explained by the package type, the package weight, and the "stowage factor".
Here, the definition of "stowage factor" is the ratio of package measurement (including
broken stowage) to package weight. Alternatively, we can use package measurement as a
substitute for the stowage factor to describe characteristics of cargo and this does not
reduce the explanatory power by much3 .
By the suggestion of Jansson and Shneerson 4,the marginal costs of different
cargoes and the cargo characteristics can be describe by:
MCijk = (aijk * m)+(bijk * w)+ Cij k
Where MCijk : the marginal cost of shipping from the ith port to the jth port an article of
the kth package type
m : package measurement
w : package weight
aijk
* m : measurement-proportional cost component
bijk * w : weight-proportional cost component
cijk : fixed cost per package unit (independent of package size)
In the common case where more than one port is called in each service range, the
tariff construction will be somewhat involved, in view of the likely possibility that some
of the coefficients, aijk, bijk, and cijk take different values for different ports for each given
package type.
2 Jansson J. 0., and Shneerson D. (1982), PortEconomics
3Jansson J. 0., and Shneerson D. (1986), Liner Shipping Economics, Chapter11
4 Jansson J. 0., and Shneerson D. (1986), Liner Shipping Economics, Chapter11
The technically most suitable tariff format would be a number of "article
matrices" of the kind shown by Fig. 2.2.3 with three entries for each pair of ports. One
entry gives the charge per m 3 , a second entry gives the charge per ton (1000kg), and a
third entry gives the fixed charge per package unit, which consequently is independent of
the package size.
Table 2.2.3
Layout of 'Article Matrix' of a Cost-Based Tariff of Freight Rates
Port of loading
First port
Second port
Port of unloading
3
a21 per m
First port
X
b2 1 per ton
C21
a12 per m 3
Second port
bl 2 per ton
X
C12
There have to be as many article matrices as there are package types. The most
insignificant package types could perhaps be grouped together under a heading like
'general cargo, not otherwise specified'. The previous example suggests that a range of
package weights be specified. An additional charge could be levied on exceptional units,
both on unusually small and unusually big units, if there are good cost reasons for this.
More important is a peak and off-peak differential. The basic freight rates should
be specified to apply to a period of time, i.e. the slack season. Outside this period, one or
more extra peak charges should be levied on top of the basic freight rates. However,
Table 2.2.3 does not show this kind of modification for this issue. Finally, a quantity
rebate may be justified. If this is the case, a simple way of making the freight rate of a
given article taper off with increases in the size of shipments is to levy a fixed charge per
shipment. This may also be justified on account of clerical work required in connection
with the documentation, etc. which is largely independent of the size of the shipment.
The logical extension of the pricing principles to containers is immediate.
Containers represent one class of package type. Only one 'article matrix' of the kind
described in the previous paragraph may be required. Freight rates for modules other than
20-foot containers should either be a multiple of the basic freight rates for 20 footers, or
the other modules could be defined as separate articles.
In fact, for container shipping, the tariff might be further simplified, without great
loss in efficiency. The fixed charge, cij, and even the charge per ton, bij, could be
superfluous; the former because it would practically be identical to all containers, and the
latter because for containerizable cargo, the effect of weight will be nil in most cases.
The tariff would then have a single charge per cubic meter that will vary according to the
ports of loading and unloading. For example, company A is an automobile company and
plans to use containers to ship its cars from Yokohama, Japan to Long Beach, California.
Company B is a lumber company and also plans to use containers to ship its lumber from
the same origin to same destination. Because of the volume constraint of containers and
because of safety reasons, company A just can use a 20-feet container to hold one car and
nothing else. Company B also uses 20-feet containers to ship its lumber. Because both
of them use the same size of containers, even though the size, the weight and the value of
their cargoes are different, the fixed cost for one container of these two kinds of cargoes
will be similar. On the other hand, compared to the total shipping tonnage of a ship, the
difference between the weights of a container of these two kinds of cargoes is still very
small. The weight factor of cargoes can be neglected. Therefore, the critical determinant
factor is the volume of containers.
The distance between loading and unloading ports is the most important factor in
determining coefficient aj. When the distance is longer, aj will become larger. The other
factors which will affect coefficient ai are the conditions of the ports. These conditions
include geographical conditions, facility conditions, etc.
However, cost-based tariffs outlined above will look very different from the
present liner conference tariffs. The radical difference is the type of commodity.
Commodity type is chief determinant in the current freight rate system, but is not so
important in the proposed tariff system. It is mainly in cases where commodities are
handled in loose form that the commodity type is a principal cost factor. Packaged cargo
is very dominating so far as liner shipping is concerned. Indirectly the type of commodity
will play a role for the freight rates, in so far that the measurement and weight of
packages are influenced by the content. There may be some other intrinsic commodity
qualities affecting the freight-rate structure.
In fact, it is difficult to construct the proposed tariff tables. The reason is that
some factors discussed before are difficult to quantify, such as conditions of ports. On
the other hand, the data of the these factors fluctuate frequently and is difficult to collect.
Therefore, it is really difficult to include these factors and obtain a precise mathematical
model, even for the cost matrix mentioned before, for marginal cost or freight rate of all
commodities.
In this thesis, because the focused commodity is containerized motor vehicles as
noted previously, the model can be simplified and just related to shipping distance and
number of containers. That is
Total cost = Unit container shipping cost (20-foot or 40-foot)
* Total number of containers ( 20-foot or 40-foot)
* Shipping distance
The unit container shipping cost is calculated by Drewry Shipping Consultants.
Drewry derived this coefficient by analyzing annual cost of a ship in Pacific trade and
Atlantic trade. The results of these two cases are listed in Exhibit 2.2.4 and 2.2.5
Table 2.2.4
Costs per Ship (2,800 TEU) on Annual Basis in Cross-Pacific Trade
(Derived by Drewry Shipping Consultants, 1992)
tons per teu
cost per teu-mile ($) cost per ton-mile ($)
5
0.208
0.042
6
0.208
0.035
7
0.208
0.030
8
0.208
0.026
9
0.208
0.023
10
0.208
0.021
11
0.208
0.019
12
0.208
0.017
13
0.208
0.016
14
0.208
0.015
15
0.208
0.014
In Pacific trade case shown in Exhibit 2.2.4, the data are based on a six ship
service, traveling a 42-day route and calling once per week at each port. The exhibit
shows the expenses for one ship traveling a complete cycle, stopping at a total of 8 ports
between Singapore and Los Angeles. The port rotation is as follows: Singapore, Hong
Kong, Kaohsiung, Busan, Kobe, Tokyo, Los Angeles, Oakland, Tokyo, Kobe, Busan,
Kaohsiung, Hong Kong, Singapore. The ship moves eastbound with 2288 teu and
westbound with 1806 teu. Therefore, the average of shipped containers per round trip is
4094. The total mile per Pacific crossing is 8275. Drewry uses the following equation to
get cost per teu-mile and cost per ton-mile in Exhibit 2.2.4:
$60,469,000 /year
$0.208 /teu-mile =
(4094 teu /round trip)*(8.57 round trips /year)*(8275 miles)
Here, 1 teu-mile represents that one 20-foot container is shipped in one nautical
mile.
For Atlantic trade, Drewry uses an example with four vessels of 1600 teu, sailing
on a 28 round-trip cycle and calling once a week at each port in the service. The port
rotation for each individual ship is Antwerp, Felixstowe, Bremerhaven, Rotterdam, Le
Havre, New York, Baltimore, Norfolk, Charleston, New York and Antwerp. Each ship
moves westbound with 780 teu and eastbound with 1200 teu. With the same
methodology, Drewry can calculate cost per teu-mile in Atlantic trade.
From Exhibit 2.2.4 and 2.2.5, given the total weight of cargoes or total amount of
containers, origin and destination, we can get total cost for sea shipping. For example,
a car company wants to ship 1,000 cars from Yokohama to Los Angeles. One container
can hold just one car. The distance between these two ports is 4,385 nautical miles.
From the Exhibit 2.2.4, the shipping cost per teu-mile is $0.208. The total shipping cost
will be $912,080. (= 1,000*4,385*0.208 )
Table 2.2.5
Costs per ship (1,600 TEU) on annual basis in Cross-Atlantic Trade
(Derived by Drewry Shipping Consultants, 1992)
teu per
round trips
cost per
yearly cost
miles per
round trip
per year
round trip
per ship
crossing
1980
12.9
$3,023,000
$38,867,143
4625
tons per teu
cost per teu-mile ($)
cost per ton-mile ($)
5
0.330
0.066
6
0.330
0.055
7
0.330
0.047
8
0.330
0.041
9
0.330
0.037
10
0.330
0.033
11
0.330
0.030
12
0.330
0.028
13
0.330
0.025
14
0.330
0.024
15
0.330
0.022
2.3
Railroad Shipping
In rail shipping industry, because (1) different commodities require different
shipping equipment and cars, (2) the operation costs for different equipment and cars are
different, and (3) the demand elasticity of different commodities is different, there is
price-discrimination among shippers of different commodities. In fact, such
discrimination was officially sanctioned under the value-of-service pricing philosophy
that the Interstate Commerce Commission (ICC) adopted shortly after its founding in
1887. Every year, ICC collects data of pricing policy and performance of railroad
shipping. The objectives of ICC are: (1) prohibit personal price discrimination (2)
guarantee value-of-service pricing (3) maintain carriers' profitability. Though there was a
deregulation in rail shipping industry in 1980, the phenomenon of discrimination
continued. We can see this pattern by Table 2.3.1.
Table 2.3.1
U.S. Railroad Revenue Per Carload (in 1982 dollars)
(Source: AAR, Analysis of Class 1 Railroads, 1980-1990. )
Year
1980
1982
1984
1986
1988
1990
Grain
1821
1531
1353
1120
1088
1101
Coal
1039
1143
1106
1023
967
900
Stone gravel
809
889
722
596
516
503
Food Products
2358
2280
2033
1797
1596
1454
Primary Forest
545
579
571
526
530
547
Lumber wood
3086
3215
3040
2553
2465
2187
Pulp paper
2101
2326
2289
2133
2015
1863
Chemicals
2572
2722
2465
2370
2157
1955
Metal Products
2114
2196
1858
1816
1759
1539
Motor vehicles
2587
2711
2518
2445
2409
2243
All carloads
1470
1477
1372
1266
1156
1064
The table reports real revenue per carload by commodity type for Class 1 railroads
during the period 1980-1990. These are proxies for real rates, since they do not reflect
distance traveled that does vary among commodities. Nevertheless, the revenue per
carload data do provide a broad indicator of relative rates, because they are based on
gross industry revenue by category and total carloads by category.
The data show that while real rates on most commodities have been falling since
deregulation, the structure of rates has remained fairly constant. Shippers of high-value
products such as automobiles and lumber continue to pay premium prices for freight
service, while bulk shippers pay lower rates. Grain and coal shippers are in the middle.
To calculate these railroad rates more precisely, we begin with revenue
observations from the ICC Waybill Sample. Each year, railroads are required to submit
to the ICC a stratified sampling of the documents which accompany rail freight
movements. (The stratification controls for multi-car movements.) This Waybill Sample,
used by officials at the ICC, provides details on about 400,000 movements including
commodity shipped, distance, equipment, and rate.
The focus of the STL is car-type miles, specifically, the miles of seven car-types
which where aggregated from the detailed information in the AAR Analysis. The
Waybill reports nominal revenue per movement along with equipment in the AAR
Analysis. The Waybill reports nominal revenue per movement along with equipment
identification, commodity identification and mileage. These are translated into real rates
(in 1982 dollars) per car-type-mile using the Producer Price Index; these are reported in
the first row of table 2.3.2 as RRATE. The distribution of these per-mile rates conforms
to the distribution of carload rates reported in table 2.3.1. Chemical and automotive
shipments command the highest rates. Coal and grain are in the middle range. Stone and
gravel are lowest.
The rates recorded here are for loaded car-miles. In fact, the railroad must absorb
the costs of the loaded and empty portions of each movement. Methodologically, too, the
marginal cost estimated to which these rates will be compared are calculated on an overall mileage basis, because the expenditure data (the left hand side in the cost regression)
cannot be segregated to reflect empty and loaded movements. To reflect these facts, we
calculate for each car-type an "effective rate", i.e., an estimate of the distributed rate the
railroad typically receives for moving a particular car-type one mile.
Table 2.3.2
Railroad Freight Rate Data for 1988 ($/car-miles)
Base Rate
Load/Empty
Adjustment
Efficient
Ratio
Coefficient
Rate
RRATE
L/E
FACTOR
FRATE
Closed hopper
2.3110
1.0310
0.5080
1.1730
Open hopper
2.2738
1.1040
0.5250
1.1930
TOFC/COFC flat cars
1.1302
15.940
0.9410
1.0630
Multilevel flat cars
2.4014
1.3350
0.5720
1.3730
Tank cars
3.0349
0.9420
0.4850
1.4720
Equipped gondola
1.5988
1.0180
0.5040
0.8057
Box cars
1.9489
1.112
0.527
1.026
We begin by calculating the ratio of loaded to empty miles by car-type and year
based on data in the Analysis. These ratios are reported in the second line of table 2.3.2.
The very high utilization for TOFC/COFC flat cars reflects two facts: (1) railroads often
receive payments for moving empty containers, and (2) there is a national fleet of
intermodal cars that is jointly utilized. Utilization of multi-level flat cars also is relatively
high because of national pooling arrangements. Tank cars have low utilization, because
they are highly specialized and frequently owned by individual chemical manufacturers.
We use the UL/E ratio to form a "payment factor" (FACTOR) which converts the
loaded car-mile rate into an effective (i.e., distributed) car-mile rate. The formula for the
payment factor is simply:
FACTOR = L/(L+E)
The payment factors are reported in line 3 of table 2.3.2, and effective rates
(FRATE), the product of FACTOR times RRATE, are given in line 4.
The adjustment from RRATE to FRATE causes some rate compression (rates of
tank cars move closer to rate of multilevel flat cars) and a change in ordering (closed
hopper drops below open hopper), but the basic rate structure is the same. The structure
is such that auto and chemical shippers, on average, pay 20-25 percent more per car-mile
than coal or grain shippers, while box-car and intermodal shippers pay 20 percent less.
The question, of course, is whether the cost of providing service differs in the same way
and to the same extent.
2.4
Trucking
The regulated trucking market can be segmented into two distinct groups:
common carriers of general commodities and common carriers of specialized
commodities. General commodity carriers tend to specialize in relatively small
shipments and hence are characterized by less-than-truck-load (LTL) carriage and
terminal consolidation. As LTL carriers of general commodities, their traffic
encompasses the spectrum of manufactured and related goods that are suitable for truck
transport, and their customers tend to be relatively small shippers who do not generate a
sufficient volume to support rail or full-truckload operations. In contrast, the carriers of
specialized commodities utilize full-truckload (TL) operations and perform few, if any,
consolidation functions. While their traffic also includes the spectrum of manufactured
goods, these carriers tend to compete directly with the railroads and hence concentrate on
large-load, long-haul traffic.
The difference in technologies between the two sectors results primarily from
differences in operating authorities. While operating authorities for specialized carriers
are more restrictive in specifying commodities for transport than are common general
freight operating authorities, specialized carrier authorities are less restrictive in routing
entitlements. Carriers of specialized commodities have authorities to carry products from
one region to another region. Thus, if a specialized product is manufactured, processed,
or distributed at one plant, an operating authority for a specialized carrier might enable
the carrier to distribute the product to all major consumers in a given region. This type of
operating authority does not require the capital-intensive consolidated terminal
distribution systems that are necessitated by operating authorities for carriers of general
freight, because operating authorities of special commodity carriers allow for direct
delivery to large consumers. At the same time, without consolidated terminal distribution
systems, small shipments of less-than-truckload freight become quite costly. In contrast,
carriers of general freight have operating authorities that allow service to a mapping of
specific points, but not to entire regions. Pointwise authorities encourage consolidated
pickup and distribution centers at each point of operating authority. Consolidation
terminals permit carriers of general freight inasmuch as more than one shipment can be
made per haul. The expenses of terminal usage, both in overhead and delay of delivery,
make general freight carriers more expensive than specialized freight carriers for large
truckload shipments. For large shipments, particularly for medium or long distances, rail
carriers and trucking carriers of specialized commodities compete for the same market.
Because regulated trucking firms are typically limited in the commodities which
they can carry or the routes they can cover, by acquiring firms with different operating
rights, regulated trucking firms can obtain longer hauls, higher load factors, fewer empty
backhauls, and thus lower operating costs. Consequently, larger firms may be more
profitable not because their costs are inherently lower, but because they can obtain higher
utilization of equipment through diversified operating rights. Since many of the recent
mergers have been characterized by the extension of operating rights and authorities, this
indicates that if economies of scale do in fact exist, they may be of a regulatory rather
than a technological nature. This implies, of course, that economies of scale would not
exist if any carrier were free to carry any commodity to any place along any route.
According to Friedlander and Spady 5 , the general specification used to describe
trucking costs and technology is
C=C(
where
, w, t)
w : factor prices (labor, fuel, capital and purchased transportation)
t : operating characteristics
V-
: transportation output
The assumptions of the general model are:
5 Friedlaender, Ann F. and Spady, Richard H. (1980), Freight TransportRegulation, Appendix C, E.
1. Firms are able to make optimal adjustments in capacity.
2. The general cost function is the linear combination of logarithm of its factor prices.
The cost function for specialized commodity carriers can be described in more
specific form:
InTC(V,w,t) = a 0 + Y. a i In wi +
+ V2 *
f jIntj + y InW
Aij In wi In wj + I I Bij In wi In tj
+ E Ci In wi In W + V2
1
Dijln ti In tj
+ E Ei In ti In V +V2Fln V In VF
where:
TC : total cost for the firm
wl : the price of labor
w2 : the price of fuel
w3 : the price of capital
w4 : the price of purchased transportation
tI : average load (tons/truck)
t 2 : average length of haul
t3 : insurance per ton-mile
V/ : output in ton-miles
a i
j, ,"
Aij,
Bij, Ci, Dij, Ei, F:coefficients ( i= 1,2,3,4, j=1,2,3)
From total cost function, the functions of marginal cost and average cost can be derived:
MC =
TC/O
=TC*w I4
and
AC=TC*w 4 /
I/
*[ y +XCinwi+EEjlntj+Fln
iY ]
From this point, it would be simple to calculate the total, average and marginal
costs for any firm in the sample. All one need to do is to substitute that firm's output,
factor prices, and technology variables into the above equations. The values of all the
factors and coefficients are shown in Table 2.4.1 and 2.4.26
Friedlander and Spady segmented trucking market as fully as possible on a
regional basis and thus analyze geographic differences in trucking technology on a
relatively desegregate scale. Because of limitations in the number of observations, all the
factors and coefficients were settled on a somewhat more aggregate breakdown for
econometric analysis: regional carriers in the Official Territory, which comprises the New
England, Middle Atlantic, and Central Regions; all other regional carriers, which
comprise carriers in the Southern, South-Western, and Western Regions, and which we
refer to as carriers in the South-West Region for notational simplicity. Although it was
recognized that this lever of aggregation may introduce some bias, this bias is minimal.
According to Table 2.4.3, the result of marginal cost is equal to $ 1.1654 per tonmile in official region, and $ 1.1358 per ton-mile in south-west region.
6 Data Source: Friedlaender, Ann F. and Spady, Richard H. (1980), Freight TransportRegulation,
Appendix C, E.
Table 2.4.1
Weighted Average Used in the Trucking Cost Function
Variable
Units
Official Region
South-West
Region
w]
w2
w3
w4
1/
Manufactures Output
$(000)/man-year
$/standardized vehicle-mile
Index
$/standardized vehicle-mile
ton-miles (000)
31.868068
0.064234
6.229777
0.357404
48,632.005
30.956642
0.116276
5.753408
0.345916
79,118.917
tons/vehicle
miles
$(000)/ton-mile
14.3419
329.4268
0.000539
14.773176
457.31263
0.000417
tons/vehicle
miles
$(000)/ton-mile
20.795576
146.75698
0.000539
21.996293
218.21319
0.000417
tI
t2
t3
Bulk Output
tI
t2
t3
Table 2.4.2
Coefficient Estimates for Cost Functions of Official, South-West
and Interregional Carriers
Coefficient
Variable
Official
South-West
Constant
8.8839
9.5417
a I
In w,
0.3485
0.2675
a 2
In w2
0.0423
0.0498
a
In w3
0.2925
0.3003
In w4
0.3167
0.3841
fl
In t1
-0.8441
-0.5346
f6 2
In t2
-0.1767
-0.1769
fl
In t3
0.0319
0.2679
a
a
4
Y,
In 9J
0.7873
0.9362
All
/2*( In W)2
0.1223
0.0781
A 12
In w 1 In w 2
-0.0099
0.0136
A 13
In w1 In w 3
-0.0136
-0.0035
A14
In w 1 In w4
-0.099
-0.0882
A22
/2( In W2) 2
-0.0178
-0.0122
A23
In w 2 In w3
-0.0258
-0.0192
A 24
In w 2 In w 4
0.0535
0.0178
2
A 33
1/2*( In w3)
-0.0343
-0.0386
A3 4
In w3 In w 4
0.0737
0.0613
A44
/2( In w 4) 2
-0.0282
0.009
Bll
In wI In t1
-0.0059
-0.0311
B12
In w1 In t2
-0.0046
-0.0244
B13
In w3 In t3
0.0055
-0.0103
B21
In w2 In t1
-0.0054
-0.0046
B2 2
In w2 In t2
0.0096
0.0073
B2 3
In w2 In t3
-0.0086
-0.0067
B3 1
In w 3 In t1
0.0184
-0.0199
B32
In w3 In t2
0.0079
-0.0123
B33
In w 3 In t3
-0.0016
0.0036
B41
In w4 In t1
-0.0071
0.0158
B42
In w4 In t 2
-0.0129
0.0293
B43
In w4 In t3
0.0046
0.0134
Cly
In wI In VJ
-0.0238
-0.0316
C2y
In w 2 In 5V
-0.0081
-0.0091
C3y
In w3 In VJ
-0.0396
-0.0336
C4y
In w 4 In Y'
0.0715
0.0743
0.3563
-0.1427
-0.1116
-0.0423
Dl
D12
1/2*( In t)
2
In t, In t2
D13
In t In t3
-0.053
0.0562
D22
/2*( In t2 )2
0.0646
0.133
D23
Int2 Int3
-0.0203
-0.0559
D33
1/2*( Int3)2
0.0088
0.0793
El
In t, In V
-0.2007
0
-0.0457
E2
in t2 In V
-0.0135
-0.0144
E3
Int3 In V
-0.0635
0.0155
( In
0.0273
0.0874
F
1/2*
T
Table 2.4.3
)2
Elasticity of Costs with Respect to Output and Operating
Characteristics at the Point of Approximation, Carrier of Specialized Commodities
Elasticity of cost with respect to
Official
South-West
Output( ( In C / In / )
1.1654
1.3858
Average load ( a In C/ a In tl)
-0.8841
-0.5346
Average length of haul ( a In C/ a In t2)
-0.1767
-0.1769
Insurance ( a In C/ a In t3)
0.0319
0.2679
Chapter 3 Total Cost Model of Global System and Case Analysis
In total cost model in global system, there are three major parts: (1) manufacturing
costs (2) shipping and handling costs (3) taxes and duties. According to Rosenfield1
(1995), a simple model for total cost function can be written as:
M
N
TotalCost= 1F(Y)+C*D+(C2-C1) * (D j=1
min( ZYi,Di))
i=1
(3.1)
jemarket..i
where:
: production at plant j
) : production function
: total demand
: size of market i, i = 1,N
: number of plants
: unit cost for distribution, handling, taxes, and duties within the same
market
C2
: unit cost for distribution, handling, taxes, and duties to other markets
In Equation 3.1, the first term is the term for total production costs. The second
term is the term for minimum possible costs of transportation, handling, taxes and duties
that are incurred by the demands located in the same market as plant's location. The final
term is the term for extra costs of transportation, handling, taxes and duties that are
incurred by the demands located outside the market where plants locate. If we assume
that taxes are constant for all the countries in the world and that there are no duties for
delivering to a different country, then this term basically is constituted by the extra
1Donald B. Rosenfield (1996), Global and Variable Cost ManufacturingSystems, European Journal of
Operational Research, 1996
transportation and handling costs for shipping the products out of the region where plants
locate.
This model is a very simplified model, particularly in the approach for
transportation costs. This chapter explores building up the total costs from the various
components and thus expands section 3.1.
If the assumptions 2 of a more general model, which is referred to as the
Geographic Distance Model, are accepted, transportation and distribution costs are
assumed to decrease with an increasing number of plants as markets that are closer to
their nearest plants. Therefore, for a geographic area with distance proportional
transportation costs, the total costs are proportional to M(-1/ 2 ). These costs can be
separated from the second term in Equation 3.1. Then, Equation 3.1 can be written as:
M
N
F(Y) + [C3 + C4 * M-1/2] * D + (C2 - C1) * (D -
TotalCost =
j=1
in(
i=1
Yj,Di))
jemarket..i
(3.2)
C3
: unit costs for taxes and duties
C4
: unit costs for distribution and handling with the market which has only
one plant
However, the assumptions of the Geographic Distance Model are not always valid
in most markets. In order to check how accurate this model is and to gain some insight
for global models, two cases are discussed. In these cases, I use Ford Taurus as the target
product. In Case One, according to demand in the continental U.S., I will compare the
results of the Geographic Distance Model and the real pricing policy of shipping industry
from chapter two, and check the estimation accuracy of this model. Because the target
market is in continental U.S., trucking is the only shipping mode used in this case
2Magee, John F., Copacino, William C., and Rosenfield, Donald B. (1985), Modern Logistics
Management, Chapter 10, John Wiley & Sons, N.Y
analysis. The reasons for the errors of this model are discussed. In Case Two, a global
automobile market is analyzed according to the data of demand in all countries
worldwide. The models of sea-shipping, trucking and railroad shipping in chapter 2 are
used to estimate total distribution costs and total operational costs. Thus we expand the
model implicit in equation 3.2. These costs in the global systems with one, two, and three
plants are estimated. The effects of number of plants on total distribution costs and total
operational costs are shown and discussed. In the last section, the limitations of the
Geographic Distance Model are discussed.
3.1
Case I: Comparison of the Results of the Geographic Distance Model and
the U.S. Automobile Market
There are three major assumptions of the Geographic Distance Model: (1)
Demand density should be constant in the whole area. That is, demand in unit geographic
area should be equal. (2) Transportation costs are proportional to distance. (3) Plants
should be located in the geographic center of their response area in order to minimize
total transportation costs. Therefore, total transportation costs can be expressed as
Total Transportation Costs = C4 * D * M(-m)
Here, C4 is the total transportation costs for the system which has only one plant
in the center of the area.
Table A. 1.1 shows Ford Taurus 1995 annual sales in each state of the U.S. Its
total sales for U.S. market are 642,997 cars. If the company ships all of its cars by
trucking, its total shipping costs are about $1,356 millions dollars. If we regard the U.S.
as a big system that has one big plant in Kansas City, the geographic center of the U.S.,
we can get value of C4 equal to $2109 dollars*(no. of plants) 1/2 /car. Then, by using the
Geographic Distance Model, total shipping costs for two-plant system and three-plant
system can be evaluated. Table 3.1.1 shows the results. More detailed assumptions and
calculations are shown in Appendix I.
In Table 3.1.1, plant locations are chosen according to the rule that each plant has
nearly equally large response area. Total distribution costs for 1-plant system in the
Geographic Distance Model are equal to that calculated directly from the practical
shipping pricing policy.
Table 3.1.1
The Results of Geographic Distance Model and Direct Calculation
No. of Plants
Location
Costs by Model
Costs by
Difference
Calculation ($)
Direct
($)
(%)
Calculation ($)
1
Kansas City
1,355,928,187
1,355,928,187
2
Denver
958,786,016
882,859,618
75,926,398
8.6
782,845,504
706,230,614
76,614,890
10.8
Cincinnati
Kansas City
3
Salt Lake City
Charleston, WV
According to Table 3.1.1, the difference between the results of the Geographic
Distance Model and direct calculation is quite large. That is, there are errors which
cannot be neglected when we use the Geographic Distance Model to analyze the cases in
the real world.
We can understand the reasons for the errors of the Geographic Distance Model
from the following two analyses:
a. The assumptions of even distributed demands throughout the whole region are
not satisfied in the U.S. market.
We observe this in Figure 3.1.1 and Table A. 1.1.
In Figure 3.1.1, most of the demands are located on the east part of the U.S. In
fact, more than 55% of demands are located in East Coast and Great Lake areas.
However, the area of this region just consists about 25% of total area in the U.S. It means
that the demand densities of these regions are about four times higher than the rest
regions in the U.S.
Figure 3.1.1
Geographical Distribution of Projected Demand for
Ford Taurus in the U.S.
Projected Demand
N
-E
We can also see the errors in a different way. In Table 3.1.2, we can see the total
shipping costs for one plant system which has some alternative plant locations. More
detailed calculations are shown in Table A. 1.1 & Table A. 1.4.
Table 3.1.2
Total Shipping Costs for One Plant System
with Different Plant Locations
Plant Location
Kansas City
St. Louis
Cincinnati
Detroit
Total Shipping Cost ($)
1,355,928,187
1,250,205,890
1,217,690,711
1,342,549,260
From Table 3.1.2, we can observe that the plant location for minimum shipping
costs among these four alternatives is Cincinnati, not Kansas City, even though Kansas
City is close to the U.S. geographical center. Cincinnati is located around the midpoint
between the U.S. geographical center and the East Coast. This result conflicts with the
assumptions of the Geographic Distance Model. In the model, the supplier location for
minimum shipping costs is in the geographical center of the system area.
The conflicts are also caused by unevenly distributed demand. The movement of
plant location in one direction will decrease the shipping costs for the demand in the same
direction, and increase the costs in the opposite direction. Because most of the demands
are in the east side of the U.S., if the plant location moves from the geographical center to
east side, the reduced costs on the east side are greater than increased costs on the west
side. Therefore, total shipping costs for the whole system will decrease.
Demand is not equally distributed even in each state. For each state, most of the
demands are found in the metropolitan area or the region closer to big cities or its capital.
This also induces the errors about the model estimation.
b. Shipping rates are different in different regions.
From Chapter 2, trucking rates are not constant throughout the U.S., and are the
functions of factor prices (fuel, labor, etc.). Different factor prices induce different
freight rates. On average, the freight rates in New England, Middle Atlantic, and Central
Regions, are lower than other regions in the U.S.3
On the other hand, the freight rates between big cities are lower than those in rural
regions. The reasons are, (1) the freight suppliers serving metropolitan regions are more
numerous than those in rural regions, and (2) it is easier for trucking companies between
big cities to cooperate with other trucking companies in order to increase equipment
utilization and reduce operation costs.
3 In New England, Middle Atlantic, and Central Regions, the freight rate for trucking is 1.1654 ton-mile. In
the rest regions, Southern, South-Western, and Western Regions, the rate is 1.3858 ton-mile.
3.2
Case II: Analysis for the projected demand in the world from 1997 to 2002
This case analyzes the global car market by using the following equation:
Total Costs = Production Costs + Distribution Costs + Tax
Appendix II4 shows Ford Automobile 1995 annual sales in the U.S. and other
countries. Because a global system is complex and some data are inaccessible, the
following assumptions are made to simplify this system:
a. Assumptions of annual projected demands:
1. The lifetime of target type of automobile is 5 years. Other new type of automobiles
will totally take over the market. The demand of target automobile is zero after 5
years.
2. The annual demand in every region will not change during 5 years.
3. Ford's market shares are identical in geographically close countries. Therefore, for
some countries which only have data of annual sale of all brands of cars, the specified
annul sales for Ford can be calculated according to the market share in a nearby
country which has available information of market share.
4. For the countries where the geographic distribution of demand is unknown, all the
demands in this country are located close to its capital, especially in undeveloped
countries. Therefore, Ford needs to be responsible for the costs of shipping its cars
only to the capital. However, because the demands in all the U.S. states are known,
the demands are located in the geographical center of each state.
5. The sale of the target type of automobile accounts for a fixed percentage of total sales
in all regions. For example, the sale of Taurus is 30% of Ford total sales in all states
in the U.S. and all countries.
4 World Road Statistics, 97' Edition, International Road Federation
Ward's automotive Yearbook, 1993, Ward's Communications
InternationalMotor Business, 1995, The Economist Intelligence Unit, U. K.
According to the previous assumptions for sales, sales for all regions in the world
are estimated as shown in Table 3.2.1. More detailed information about sales in each
country is shown in Appendix II.
Table 3.2.1
Annual sales of Ford Taurus in major regions in the world
Region
Sales (cars) %of total sales
Africa
13,405
1.16
Caribbean America
3,175
0.27
Central & South America
54,108
4.67
North America
545,335
47.10
Far East
68,321
5.90
Middle East
11,555
1
East Europe
17,100
1.48
West Europe
406,150
35.08
Pacific
38,569
3.33
Total
1,157,718
100
b. Assumptions of production model: The production model is
Production Costs = Fixed Costs Per Plant * Number of Plants
+ Variable Costs Per Unit * Total Demand
According to the data 5 , the setup cost for a plant with annual capacity of 250,000
cars is about $ 150 million dollars. I assume that the setup cost is constant and unrelated
to the production capacity of the plant. On the other hand, I assume that yearly
1993-1994 World Automotive Market Report, Auto & Truck International
5 Ashoka Mody & David Wheeler, Automation and World Competition
depreciation is 15%. After 5 years, the surplus value of the plant is only 25% (=100% 5*15%) of the setup cost. Therefore, fixed cost for one plant to operate 5 years is
$112,500,000 dollars. Variable cost is equal to $5,500 dollars per car. These costs are
constant during these 5 years. The other assumption is that the capacity of each plant is
infinite.
c. Assumptions of shipping:
1. Containers can be shipped only to some certain seaports. For each region, a certain
seaport is chosen according to its annual container traffic.
2. All the countries choose the closest seaport which is already determined according to
previous condition to import or export their cars.
3. For the country which has its own seaport, it will use the feeder system to transfer the
products from the regional seaport to its own seaport with no additional costs.
4. The distance between the country's capital and seaport is straight line distance.
5. All factors' prices are fixed during production period. Therefore, the coefficients in
the models will not change.
6. The models, factor prices, and coefficients of sea-shipping, railroad shipping and
trucking for all countries are same as those for the U.S. That is,
Trucking : (1.1654+1.3858)/2 = $1.2756 dollars/ton-mile
Railroad: $1.373 dollars/ton-mile
Sea Shipping: Cross-Pacific : $0.208 dollars/teu6-mile
Cross-Atlantic : $0.33 dollars/teu-mile
6Teu is the unit for one 20-feet container. 1 teu = 1 20-feet container.
d. Assumptions of taxes: The tax rate is a constant and equal to 35%, the current U.S.
corporate tax rate, in all countries. Net profit of a car is close to its variable production
cost. Therefore,
Unit Tax Costs = 35% * Unit Variable Production Costs
= 0.35 * $5,500 dollars/car = $1,925 dollars/car
Analysis for plant locations:
1. From Table 3.2.1, about half of the total demands of the target cars in the world are
located in North America, and about 35% are located in Western Europe. According
to the total cost model, in order to avoid additional costs for shipping and handling, if
the company just wants to set up one plant, it should be located in North America. If
the company wants to set up two plants, the first plant should be in North America,
and the second one should be in Western Europe.
2. There are two alternative locations for the third plant: Far East or North America.
3. In case one, if the company just sets up a plant in the U.S., we can see that the plant
location for minimum shipping costs in the U.S. is Cincinnati. However, if the defined
system is North American, which includes the U.S., Canada, and Mexico, the optimal
location will change. The annual sales of target car are 38,269 in Canada and 8,661 in
Mexico. Because the annual sales in Canada are more than that in Mexico, the
optimal location will move to the region around Detroit in the north..
4. Table 3.2.2 shows the demand distribution in Western Europe. Following the similar
analysis in the U.S., if the second plant is located in Western Europe, it should be
located in the middle of France.
Table 3.2.2
Demand Distribution in Western Europe
Country
U. K.
Spain
Germany
France
Italy
% of total sales in W. Europe
30.92
9.51
24.12
11.19
11.53
5. Table 3.2.3 shows the demand distribution in Far East. If the third plant is located in
the Far East, it should be in Taiwan.
Table 3.2.3
Demand Distribution in the Far East
6. If the third plant is located in North America, according to the demand distribution in
the U.S., Canada, and Mexico, these two plants should be in Detroit and Salt Lake
City.
7. According to the previous conclusions, I used the projected demand of all countries in
the world to calculate total costs for the systems with one, two and three plants. Table
3.2.4 summarizes these results..
Table 3.2.4
Plant Locations in the Systems with Different Numbers of Plants
System
1 Plant
2 Plants
Plant Locations
Detroit, U.S.
(1) Detroit, U.S. (2) Paris, France
3 Plants - Alternative 1
(1) Detroit, U.S. (2) Paris, France (3) Taiwan
3 Plants - Alternative 2
(1) Detroit, U.S. (2) Salt Lake City, U.S. (3) Paris, France
* Results of analysis
The cost components and their percentage ratios are shown in Table 3.2.5 & 3.2.6
Table 3.2.5
Costs for 1-Plant & 2-Plant System (for 5 years )
One Plant System
$
Costs
%
Two Plant System
$
%
112,500 K
0.19
225,000 K
0.41
Production (variable)
31,837,245 K
52.93
31,837,245 K
57.35
Distribution
17,052,400 K
28.35
12,310,122 K
22.17
Taxes
11,143,035 K
18.53
11,143,035 K
20.07
Total
60,145,180 K
100
55,515,402 K
100
Production (fixed)
Table 3.2.6
Costs for Two Alternatives in 3-Plant System (for 5 years)
Location : Taiwan
Costs
$
%
Location : U.S.
$
%
337,500 K
0.63
337,500 K
0.64
Production (variable)
31,837,245 K
59.72
31,837,245 K
60.41
Distribution
9,990,267 K
18.74
9,388,126 K
17.81
Taxes
11,143,035 K
20.91
11,143,035 K
21.14
Total
53,308,047 K
100
52,705,906 K
100
Production (fixed)
* Observations
1. From Table 3.2.5:
* Under the previous assumptions, because total demands for 5 years are fixed,
variable production costs and taxes are unrelated to the numbers of the plants
in the systems.
* Fixed production cost is increased by $112,500,000 dollars from one-plant
system to a two-plant system
* Distribution and Handling costs are decreased by $4,742,278,000 dollars
2. Because variable production costs and taxes are constant, the optimal number of
plants depends on the fixed production costs and distribution costs.
3. In a one-plant system, building the second plant reduces distribution costs. The
reduced parts are more than the induced extra fixed production costs of the second
plant. Therefore, total operational costs in a two-plant system are less than those in a
one-plant system.
4. Table 3.2.6 shows that the system with the third plant in Salt Lake City, U.S. has less
operational costs than the other alternative with the third plant in Taiwan. The reason
is that most of the demands are located in North America. Building an additional
plant in Taiwan reduces much distribution costs for unit demand in the Far East and
the Middle East; because the demands in these regions consist just 6.9% of total
demands in the world, the total distribution costs are not reduced so much as the
amount reduced by building an additional plant in Salt Lake City.
3.3
The Limitations of the Application of the Geographic Distance Model
Based on previous analysis, we know that there are limitations of application of
the Geographic Distance Model. Any application would have to satisfy the following two
conditions to avoid estimation errors:
1. Transportation costs would have to be proportional to shipping distances.
2. Demand would have to be equally distributed throughout the whole
geographic regions.
In practice, most global markets do not satisfy these conditions. Only regional
markets with small areas might satisfy these assumptions. The reasons are:
1. Transportation Costs are more likely to be proportional to shipping distances in
regional markets. In regional markets, the choices of transportation modes are
limited. For example, for the cargo shipment with a small country, such as
Switzerland, trucking and railroad shipping are the only economic choices. In
addition, the freight rates are more likely to stay constant, because the factors which
determine freight rates are more likely to stay constant in a small region. On the other
hand, in global systems, four different transportation modes are available. Each of
them has its individual pricing. Also, there are more factors which will affect freight
rates. These make the transportation costs in global systems more complex and not
proportional to shipping distances.
2. Demands are more likely to be equally distributed throughout the whole
geographic regions. In regional markets, two conditions are more likely to be
satisfied in the whole regions: (1) evenly distributed populations (2) evenly
distributed incomes. These two conditions cause equally distributed demand
throughout the whole regions.
In section 3.1, although the income of each person is similar in the U.S., because
of unequally distributed population in the U.S., the demand distribution is still uneven in
the U.S. market. It induces 10.8% error as shown in Table 3.1.1.
The markets outside of the U.S. are even more complex and heterogeneous
than those in the U.S.. It is more difficult for them to satisfy the conditions of the
Geographic Distance Model. For example, there are big income and population gaps
between the countries in Western Europe and Eastern Europe, even though these
countries are geographically close. Therefore, applications of the Geographic Distance
Model in European market will yield greater errors than those in the U.S. market. In
global markets, these gaps of population and income are larger between different
locations, and the estimation accuracy of the model will be much lower.
In conclusion, we can use a model such as the Geographic Distance Model only as
an approximation to part of a total cost model.
Chapter 4 Summary and Conclusion
The major objective of the thesis has been to understand the characteristics of the
pricing behavior in different transportation modes and try to develop cost models for
them. Simplified models, such as the Geographic Distance Model, are evaluated by
comparing the results of these cost models.
The major results of this research are the pricing models in Chapter Two. Such
models are effective tools in tabulating total system costs, as shown by the examples in
Chapter Three.
Based on the analysis in Chapter 2 and Chapter 3, we know that there are limits to
the Geographic Distance Model. One possible way to mitigate estimation errors is
through market segmentation. We need to divide the large markets into several submarkets in order to create homogenous conditions within the markets. Such market
segmentation is an area of future research.
The applications of the cost models showed the following:
1. In the example for the automobile industry, a decentralized system has less
operational costs than a centralized system. From Tables 3.2.5 & 3.2.6, we get the
following results:
Table 4.1
Change of Plants, Fixed Production Costs, and Distribution Costs
Change of total plants in the system
Increased fixed Production Costs
Decreased Distribution Costs
1- 2
24 3
$ 112.5 M
$112.5 M
$4,727.3 M
$2,922.0 M
Under the assumptions in section 3.2, variable production costs and taxes are
unrelated to the total number of plants in the system. From Table 4.1, when the
number of total plants increases by one, increased fixed production costs are much
less than decreased distribution costs. As such increasing, the total number of plants
will decrease total operational costs, and the decreased distribution costs will also
decrease. When there are sufficient plants in the system, the decreased distribution
costs will be equal to the increased fixed production costs, and the company will get
the minimum total operational costs. If the number of plants in the system is more
than the optimal number, the decreased distribution costs will be less than the
increased fixed production costs, and the total operational costs will increase.
2. Because of the characteristics and freight rates of railroad shipping and trucking are
quite similar, these two transportation modes compete with each other. The biggest
differences between these two modes are the convenience and reliability. Because
trucking has more flexibility than railroad shipping, it is easy for trucking carriers to
offer door-to-door service. On the other hand, railroad shipping schedule is less
affected by uncontrolled factors, such as, weather, traffic congestion, etc.. Railroad
shipping thus has higher reliability than trucking. The shippers choose between these
two modes according to the characteristics of their products and their requirements for
the shipment.
3. The freight rates for sea shipping are about 1/6 of those for trucking and railroad
shipping. A company can have lower distribution costs by avoiding shipment via
trucking and railroad. Locating plants close to seaports will, therefore, provide
economic advantages.
Any conclusions need to be subject to the following qualifications:
1. Different countries have their specific pricing policy and structures for their shipping
modes. Some of them are similar to those in the U.S., but some of them are quite
different. Because of unavailable freight rate data in many countries, it is difficult to
precisely estimate all the coefficients for the shipping modes in different countries.
Using the models in the U.S. for other countries will induce some error.
2. In the production model, I assume that production costs consist of a fixed and a
variable component, and the plants have unlimited capacity. However, this
assumption is only an approximation. When plant capacity increases, the production
costs should vary in a nonlinear manner that is more complex than a fixed and a
variable cost.
3. From previous results, we can know that demand locations are very important factors
in determining unit distribution costs and total costs. Accurate estimation of costs
requires geographical distribution of demands.
4. In railroad shipping and trucking industries, especially in railroad shipping industry,
the carriers offer discounts on their freight rates for long distance shippers. Because
the calculations of these discounts are complex and different in different cases, I just
assume that there is no discount on the freight rats in this thesis. In fact, the longdistance shipping costs for railroad shipping and trucking are lower than the results of
Chapter 3.
Bibliography
* Chapter 1:
1. Arntzen, Bruce C., and Brown, Gerald G. (1995), Global Supply ChainManagement
at DigitalEquipment Corporation,Interfaces 25, January-February 1995.
2. Huchzermeier, Arnd, and Cohen, Morris A. (1996), Valuing OperationalFlexibility
Under Exchange Rate Risk, Operations Research, Vol. 44, No. 1, January-February
1996.
3. Rosenfield, Donald B. (1996), Global And Variable Cost ManufacturingSystems,
European Journal of Operational Research, 1996
* Chapter 2:
1. Bryan, A.I. (1974) Regression analysis of oceanfreight rates on some Canadian
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2. Drewry Shipping Consultants, LTD, ContainerMarket Profitabilityto 1997: Will
stabilizationagreements save carriersfrom checkmate?, London, 1992
3. Evans, J.J. (1977) Liner FreightRates Discriminationand Cross-subsidization,
Maritime Policy and Management, 4, 41
4. Friedlaender, Ann F. (1969), The Dilemma of Freight TransportRegulation,
Washington DC: The Brookings Institution
5. Friedlaender, Ann F. and Spady, Richard H. (1980), FreightTransportRegulation,
The MIT Press, Cambridge, Massachusetts
6. Gerard J. McCullough (1993), Essays on the Economic Performance of U.S. Freight
Railroads UnderDeregulation,Massachusetts Institute of Technology Thesis.
7. Gerald, Thomas M. (1980), Market Structure and the Industry Behavior of the
General Commodity Carriers,Thesis, Massachusetts Institute of Technology,
Cambridge
8. Jansson, J.O. and Shneerson, D. (1982), PortEconomics, MIT Press, Chicago
9. Jansson, J.O., and Shneerson, D. (1986), Liner Shipping Economics, Chapman and
Hall, Chapter 3, 4, 11
10. Keeler, Theodore E. (1974), Railroad Costs, Returns to Scale, and Excess Capacity,
Review of Economics and Statistics. 61:201-208
11. Lewis, Dale B. (1995), FreightMode Choice: Air TransportVersus Ocean Transport
in the 1990s, Massachusetts Institute of Technology, M. S. Thesis
12. Lipsey, R. G. and K. Lancaster (1956), The General Theory of Second Best, Review
of Economic Studies 24.
13. MacAvoy, Paul W. and Snow, John W. (1977), Regulation of Entry and Pricingin
Truck Transportation,American Enterprise Institute for Public Policy Research,
Washington, D. C.
14. Meyer, J. R., M. J. Peck, J. Stenason, and C. Zwick (1959), The Economics of
Competition in the TransportationIndustries,Harvard University Press, Cambridge,
Massachusetts
15. Moore, T. G. (1975), DeregulatingSurface Freight Transportation,in A. Phillips
(ed.), Promoting Competition in Regulated Markets, The Brookings Institution
(Washington DC)
16. Roy Pearson (1985) ContainerShips and Shipping, Chapter 8
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Press, Inc., Connecticut
Chapter 3:
1. Ashoka Mody & David Wheeler (1990), Automation and World Competition, New
Technologies, IndustrialLocation and Trade, St. Martin's Press, New York
2. InternationalMotor Business, 1995, The Economist Intelligence Unit, U. K.
3. Rosenfield, Donald B. (1996), Global And Variable Cost ManufacturingSystems,
European Journal of Operational Research, 1996
4. World Road Statistics, 1997 Edition, International Road Federation
5. Ward's automotive Yearbook, 1993, Ward's Communications.
6. 1993-1994 World Automotive Market Report, Auto & Truck International
Appendix I
The following numbers and formulas are used in the calculation procedure.
Weight of 4-door Taurus : 3326 lb. = 1.66 tons
Freight rate for trucking : Official : $1.1654 ton-mile
South-West: $1.3858 ton-mile
Transportation Cost = State Annual Sales (cars/year)
* Weight of Unit Car ( tons/car )
* Distance between Plants and Demand ( miles )
* Freight Rate by truck ( $/ton-mile )
Note:
1. In the calculation of Table A. 1.2 & Table A. 1.3, each state will get cars from the
closest plant.
2. The distance for each state is measured from the geographic center of the state to the
supply plant location.
3. There is no limitation on the capacity of the plant production.
Data source:
1. World Road Statistics, 1997 Edition, International Road Federation
2. The Automotive News, 1996, Crain Communications, Inc.
3. Ward's automotive Yearbook, 1993, Ward's Communications
4. Chapter 2
Table A.1.1
Transportation Cost in the U.S. with One Plant
(Plant Location : Kansas City)
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Dist. of Columbia
Federal Government
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
Sales
8210
10244
4247
70164
9791
8576
2001
610
1410
47653
22145
1612
33123
11586
4584
4789
7957
10659
2873
18736
14197
37361
12439
5259
15961
1308
2780
3729
3543
23805
3583
28118
18237
909
32801
5297
6995
27151
2045
10506
% of total
Taurus in US
1.277
1.593
0.660
10.912
1.523
1.334
0.311
0.095
0.219
7.411
3.444
0.251
5.151
1.802
0.713
0.745
1.237
1.658
0.447
2.914
2.208
5.810
1.934
0.818
2.482
0.203
0.432
0.580
0.551
3.702
0.557
4.373
2.836
0.141
5.101
0.824
1.088
4.222
0.318
1.634
Distance to
Kansas City
692
1250
391
1750
616
1204
1134
1067
1067
1231
792
1482
500
487
197
245
500
842
1600
1041
1529
777
443
637
100
1117
492
1450
1550
1228
769
1250
1036
965
663
350
1852
1000
1250
1221
$ by truck
13069468
29455543
3819588
282462653
13874470
19974184
4389785
1259150
2909461
134945046
40346839
5495686
32038798
10915078
1747004
2699104
9152253
20646061
8892804
37732077
41993993
56158668
12675946
7705662
3671598
3361008
3146433
12438529
10622449
56552220
6338431
67993879
36550747
2016791
42071085
4264878
29799316
52524380
4944020
24815069
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
W. Virginia
Wisconsin
Wyoming
Total
Table A.1.2
1046
12043
44257
3868
1546
18198
10691
3523
10135
708
0.163
1.873
6.883
0.601
0.240
2.830
1.663
0.548
1.576
0.110
642997
100.000
694
538
652
1137
1300
1086
1906
739
562
853
1669138
14904162
66379397
10115781
3888087
38231792
46873735
5989157
11018481
1388303
1355928187
Transportation Cost in the U.S. with Two Plant
(Plant Location : Denver & Cincinnati)
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Dis of Columbia
Federal Government
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Sales
8210
10244
4247
70164
9791
8576
2001
610
1410
47653
22145
1612
33123
11586
4584
4789
7957
10659
2873
18736
14197
37361
12439
5259
15961
Distance to Distance to
Denver
Cincinnati
$ by truck
1297
455
8593364
790
1856
18615903
1006
601
5871029
1100
2280
177547953
250
1208
5630873
1936
778
12906906
1759
581
2249087
1681
515
607743
1681
515
1404285
1848
885
97015732
1406
453
23077169
831
1930
3081589
980
290
18582503
987
109
2443005
676
591
5241012
455
803
5012621
1106
100
1830451
1341
806
19763331
1460
991
5507980
1685
516
18702931
1990
820
22521304
1281
268
19370043
926
1214
745
697
687
490
19943870
8310502
17990831
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
W. Virginia
Wisconsin
Wyoming
1308
2780
3729
3543
23805
3583
28118
18237
909
32801
5297
6995
27151
2045
10506
1046
12043
44257
3868
1546
18198
10691
3523
10135
708
Total
Table A.1.3
553
290
900
1990
1785
457
1700
1685
768
1277
697
1249
1600
2089
1734
554
1155
1036
537
1900
1666
1313
1375
1050
248
1602
1021
2050
875
675
1508
600
509
1094
109
843
2370
437
853
618
1096
277
1061
1727
750
535
2395
205
391
1392
642997
1663955
1854605
7720466
5996544
31085300
3766792
32637062
17957848
1605073
6916664
8493201
20096839
22953154
3373799
12559961
1332424
7673704
105474012
4777638
2243127
18834262
32290249
1661404
7665883
403633
882859618
Transportation Cost in the U.S. with Three Plant
(Plant Location : Kansas City, Salt Lake City & Charleston in W. Vagina)
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Dis of Columbia
Federal Government
Sales
8210
10244
4247
70164
9791
8576
2001
610
1410
Distance to Distance to Distance to
Kansas City Salt Lake Charleston $ by truck
692
1250
391
1750
616
1204
1134
1067
1067
1673
655
1532
720
537
2284
2152
2205
2205
535
2076
733
2420
1375
509
476
359
359
10104285
15434704
3819588
116213206
12095114
8444235
1842626
423650
978910
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
W. Virginia
Wisconsin
Wyoming
Total
47653
22145
1612
33123
11586
4584
4789
7957
10659
2873
18736
14197
37361
12439
5259
15961
1308
2780
3729
3543
23805
3583
28118
18237
909
32801
5297
6995
27151
2045
10506
1046
12043
44257
3868
1546
18198
10691
3523
10135
708
642997
1231
792
1482
500
487
197
245
500
842
1600
1041
1529
777
443
637
100
1117
492
1450
1550
1228
769
1250
1036
965
663
350
1852
1000
1250
1221
694
538
652
1137
1300
1086
1906
739
562
853
2337
1920
336
1380
1545
1068
1062
1666
1856
2507
2092
2328
1649
1318
1794
1287
566
606
470
2378
2193
623
2100
2022
1049
1727
1206
757
2000
2358
2222
904
1669
1400
180
2270
2185
800
1781
1436
310
794
515
2160
483
307
757
645
247
870
941
363
720
368
937
852
620
1820
1204
2200
772
547
1527
470
299
1297
153
999
2572
350
738
489
1336
395
1199
1781
720
310
2449
150
580
1661
87040103
26235634
1245985
30949479
6880758
1747004
2699104
4521213
20646061
5230080
13157295
19774804
26597670
12675946
7705662
3671598
1703071
3146433
4031799
5290665
25190606
5135036
25565699
10548912
2016791
9708712
4264878
12180390
18383533
2918949
9938222
1669138
10942647
66379397
1601443
2153402
10913311
19674180
1215661
11018481
504541
706230614
Table A.1.4
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Dist. of Columbia
Federal Government
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
Transportation Cost for Alternative Plant Locations in the U.S. in One Plant Case
Taurus
Sales
8210
10244
4247
70164
9791
8576
2001
610
1410
47653
22145
1612
33123
11586
4584
4789
7957
10659
2873
18736
14197
37361
12439
5259
15961
1308
2780
3729
Plant Location:
distance (mile)
527
1503
405
2000
860
1103
904
830
830
992
560
1675
180
257
372
445
275
677
1332
834
1100
540
608
492
125
1396
723
1750
St. Louis
cost ($)
9953193
35417345
3956351
322814460
19370202
18298609
3499441
979470
2263217
108745317
28528068
6211386
11533967
5760113
3298911
4902454
5033739
16600217
7403259
30229157
30211506
39029190
17397235
5951626
4589498
4200508
4623722
15012018
Plant Location:
distance (mile)
455
1856
601
2280
1208
778
581
515
515
885
453
1930
290
109
591
803
100
806
991
516
820
268
697
687
490
1602
1021
2050
Cincinnati
cost ($)
8593364
43735590
5871029
368008485
27208377
12906906
2249087
607743
1404285
97015732
23077169
7157000
18582503
2443005
5241012
8846450
1830451
19763331
5507980
18702931
22521304
19370043
19943870
8310502
17990831
4820354
6529489
17585507
Plant Location:
distance (mile)
734
2041
896
2300
1282
645
597
527
527
1210
727
1965
350
286
614
992
366
1071
780
532
650
70
674
1000
660
1573
1025
2000
Detroit
cost ($)
13862701
48095010
8752816
371236629
28875115
10700456
2311024
621904
1437007
132642978
37035545
7286790
22427159
6410087
5444977
10928616
6699449
26261201
4335242
19282867
17852253
5059339
19285751
12096801
24232548
4733094
6555070
17156592
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
W. Virginia
Wisconsin
Wyoming
Total
3543
23805
3583
28118
18237
909
32801
5297
6995
27151
2045
10506
1046
12043
44257
3868
1546
18198
10691
3523
10135
708
642997
1100
999
1034
1000
747
975
433
491
2095
800
1153
889
775
324
845
1380
1060
836
2139
520
377
1084
7538512
46006244
8522676
54395103
26354641
2037690
27476289
5983015
33709270
42019504
4560364
18067647
1863951
8975741
86028514
12277729
3170286
29430735
52603840
4214292
7391401
1764267
1250205890
875
675
1508
600
509
1094
109
843
2370
437
853
618
1096
277
1061
1727
750
535
2395
205
391
1392
5996544
31085300
12429590
32637062
17957848
2286393
6916664
10272264
38134114
22953154
3373799
12559961
2635987
7673704
108019234
15364955
2243127
18834262
58899578
1661404
7665883
2265554
1217690711
716
649
1564
550
656
1105
206
1036
2364
450
694
830
1073
542
1349
1649
600
631
2230
368
367
1386
4906886
29887940
12891166
29917307
23144102
2309382
13071861
12624040
38037572
23635971
2744920
16868557
2580670
15014974
137340195
14670997
1794502
22213868
54841778
2982422
7195343
2255788
1342549260
Appendix II
The following numbers and formulas are used in the calculation procedure.
Weight of 4-door Taurus : 3326 lb. = 1.66 tons
Freight rate:
trucking in US: Official : $1.1654 ton-mile
South-West: $1.3858 ton-mile
in other countries : (1.1654+1.3858)/2 = $1.2756 dollars/ton-mile
railroad: $1.373 dollars/ton-mile
sea Shipping: Cross-Pacific : $0.208 dollars/teul-mile
Cross-Atlantic : $0.33 dollars/teu-mile
Transportation Cost = State Annual Sales( cars/year)
* Weight of Unit Car ( tons/car )
* Distance between Plants and Demand ( miles )
* Freight Rate by truck ( $/ton-mile )
Note:
1. The distance for each state is measured from the geographic center of the state to the
supply plant location.
2. In U.S., Canada, and Mexico, the products are shipped directly by trucking to the
location of demand.
3. The demand location in Canada is equally divided into Vancouver, Ottawa, Toronto,
and Montreal.
4. The total distribution costs in U.S. is calculated from Appendix I.
5. There is no limitation on the capacity of the plant production.
Teu is the unit for one 20-feet container. 1 teu = 1 20-feet container.
Data source:
1. World Road Statistics, 1997 Edition, International Road Federation
2. The Automotive News, 1996, Crain Communications, Inc.
3. Ward's automotive Yearbook, 1993, Ward's Communications
4. InternationalMotor Business, 1995, The Economist Intelligence Unit, U. K.
5. 1993-1994 World Automotive Market Report, Auto & Truck International
6. Ashoka Mody & David Wheeler (1990), Automation and World Competition, New
Technologies, IndustrialLocation and Trade, St. Martin's Press, New York
7. Distances Between Ports, 1965, U.S. Naval Oceanographic Office
8. Chapter 2
Table A.2.1
Country
Sales for
Import Port
Taurus
Distribution Cost in Global System with 1 Plant
Plant Location : Detroit, U.S.
Unit Cost from
Unit Cost from
Detroit to export Port
Export to Import Ports
Import Port to Capital
Distance
Total Cost
Unit Cost
Africa
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gambia
Ghana
Guinea
Guinea-Bissau
Ivory Coast
Kenya
Liberia
Libya
Madagascar
Malawi
428
121
176
55
50
61
134
13
18
2
12
17
12
2
13
13
20
127
8
21
504
129
5
857
4
43
Casablanca
Durban
Abidjan
Durban
Abidjan
Durban
Abidjan
Durban
Abidjan
Abidjan
Durban
Durban
Mombasa
Durban
Mombasa
Mombasa
Durban
Abidjan
Abidjan
Durban
Abidjan
Mombasa
Abidjan
Casablanca
Durban
Durban
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,001
$2,254
$1,628
$2,254
$1,628
$2,254
$1,628
$2,254
$1,628
$1,628
$2,254
$2,254
$4,564
$2,254
$4,564
$4,564
$2,254
$1,628
$1,628
$2,254
$1,628
$4,564
$1,628
$1,001
$2,254
$2,254
560
1488
352
432
400
1472
848
1120
1200
1120
1280
1664
1040
800
800
688
1040
192
800
800
0
0
352
1104
0
928
$1,186
$3,151
$745
$915
$847
$3,117
$1,796
$2,372
$2,541
$2,372
$2,710
$3,524
$2,202
$1,694
$1,694
$1,457
$2,202
$407
$1,694
$1,694
$0
$0
$745
$2,338
$0
$1,965
$1,474,847
$804,284
$639,369
$243,445
$185,589
$402,184
$624,945
$78,430
$95,688
$12,139
$77,723
$117,423
$93,493
$10,491
$98,615
$95,502
$113,956
$418,693
$35,087
$108,735
$1,453,284
$751,588
$16,384
$3,936,658
$14,299
$235,696
Mali
Mauritania
Mauritius
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Saint Lucia
Sao Tome & Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Tunisia
Uganda
Zaire
Zambia
Zimbabwe
Subtotal
23
40
63
1991
40
12
110
953
23
16
5
47
15
47
0
4161
630
42
174
68
67
48
210
252
1525
13405
Casablanca
Casablanca
Durban
Casablanca
Durban
Durban
Abidjan
Abidjan
Durban
Durban
Durban
Abidjan
Durban
Durban
Mombasa
Durban
Mombasa
Durban
Mombasa
Abidjan
Casablanca
Mombasa
Durban
Durban
Durban
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,001
$1,001
$2,254
$1,001
$2,254
$2,254
$1,628
$1,628
$2,254
$2,254
$2,254
$1,628
$2,254
$2,254
$4,564
$2,254
$4,564
$2,254
$4,564
$1,628
$1,001
$1,628
$2,254
$2,254
$2,254
1232
1040
0
0
224
848
576
352
800
800
800
832
1120
1120
448
0
1088
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$455
$455
$455
$455
$455
$455
$455
192
288
896
448
1632
912
640
$2,609
$2,202
$0
$0
$474
$1,796
$1,220
$745
$1,694
$1,694
$1,694
$1,762
$2,372
$2,372
$949
$0
$2,304
$0
$407
$610
$1,897
$949
$3,456
$1,931
$1,355
$109,638
$176,981
$219,576
$4,493,017
$157,715
$66,293
$450,652
$3,459,830
$120,210
$81,852
$25,681
$220,561
$86,458
$275,430
$0
$14,605,747
$5,117,701
$148,890
$1,082,587
$236,733
$277,415
$184,288
$1,462,787
$1,371,146
$7,417,328
$53,987,063
0
0
0
0
0
0
0
$0
$0
$0
$0
$0
$0
$0
$59,454
$47,081
$50,931
$16,327
$369,252
$431,273
$12,168
Caribbean America
Bahamas
Barbados
Bermuda
Cuba
Dominican Republic
Guadeloupe
Haiti
35
28
30
10
216
252
7
Jamaica
Martinique
Netherlands Antilles
Puerto Rico
Trinidad & Tobago
Virgin Islands
Other
Subtotal
114
0
194
1806
108
15
363
3175
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$455
$455
$455
$455
$455
$455
$455
0
0
0
0
0
0
0
$0
$0
$0
$0
$0
$0
$0
$195,100
$0
$331,156
$3,090,326
$184,164
$26,492
$620,417
$5,434,140
Buenos Aires
Cristobal
Callao
Santo's
Valparaso
Cristobal
Limon-Moin
Callao
Cristobal
Cristobal
Limon-Moin
Cristobal
Limon-Moin
Limon-Moin
Cristobal
Santo's
Callao
Cristobal
Santo's
Cristobal
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,400
$666
$1,111
$1,636
$1,542
$666
$666
$1,111
$666
$666
$666
$666
$666
$666
$666
$1,636
$1,111
$666
$1,636
$666
0
672
512
0
0
352
0
560
960
1424
480
1120
320
208
0
624
0
1280
832
640
$0
$1,423
$1,084
$0
$0
$745
$0
$1,186
$2,033
$3,015
$1,016
$2,372
$678
$440
$0
$1,321
$0
$2,710
$1,762
$1,355
$24,890,438
$1,505
$310,644
$102,438,610
$5,344,674
$3,233,246
$1,078,208
$4,050,691
$562,245
$344,818
$767,700
$61,827
$70,889
$425,937
$1,109,574
$736,623
$781,894
$1,459,185
$2,916,809
$5,504,903
$156,090,422
C. & S. America
Argentina
Belize
Bolivia
Brazil
Chile
Colombia
Costa Rica
Ecuador
El Salvador
French Guiana
Guatemala
Guyana
Honduras
Nicaragua
Panama
Paraguay
Peru
Suriname
Uruguay
Venezuela
Subtotal
9371
0
90
35424
1910
1212
561
1140
142
70
261
14
27
180
577
175
330
315
627
1680
54108
North America
Canada
38269
$0
Mexico
United States
8661
498405
$0
$0
-
Subtotal
Far East
Afghanistan
Bangladesh
Brunei
Burma
Cambodia
China
Hong Kong
India
Indonesia
Japan
Laos
S. Korea
Malaysia
Maldives
Mongolia
Pakistan
Philippines
Singapore
Sri Lanka
Taiwan
Thailand
Vietnam
Subtotal
$0
$0
$0
$3,356
$128,442,845
$6,503
$56,327,119
$1,040,647,567
$1,225,417,530
545335
I
1
19
52
0
134
6314
1099
4350
98
23404
306
3421
600
0
84
1465
2681
1565
381
19986
2340
19
68321
Singapore
Singapore
Singapore
Singapore
Singapore
Shanghai
Hong Kong
Madras
Singapore
Yokohama
Singapore
Pusan
Singapore
Singapore
Shanghai
Madras
Marila
Singapore
Madras
Kaoshung
Singapore
Singapore
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$1,636
$1,636
$1,636
$1,636
$1,636
$1,208
$1,327
$1,966
$1,636
$1,966
$1,636
$1,966
$1,636
$1,636
$1,208
$1,966
$1,360
$1,636
$1,966
$1,360
$1,636
$1,636
0
0
0
0
0
592
0
560
0
0
0
160
0
0
1344
1280
0
0
0
0
80
0
$0
$0
$0
$0
$0
$1,254
$0
$1,186
$0
$0
$0
$339
$0
$0
$2,846
$2,710
$0
$0
$0
$0
$169
$0
$7,973
$132,709
$355,433
$1,502
$922,336
$48,516,841
$7,197,526
$36,424,280
$674,786
$168,218,238
$2,097,005
$25,749,295
$4,112,944
$3,210
$781,443
$14,500,167
$17,645,124
$10,734,447
$2,741,279
$131,543,751
$16,444,901
$126,948
$488,932,138
Mid East
Subtotal
11555
Jeddah
$1,256
$3,785
560
$1,186
$71,948,240
318
631
411
176
4563
188
741
948
40
1272
385
883
71
1454
3200
972
641
206
17100
Piraeus
Hamburg
Piraeus
Piraeus
Hamburg
Limassol
Hamburg
Hamburg
Piraeus
Hamburg
Hamburg
Hamburg
Piraeus
Hamburg
Piraeus
Hamburg
Piraeus
Piraeus
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,323
$1,254
$1,323
$1,323
$1,254
$1,523
$1,254
$1,254
$1,323
$1,254
$1,254
$1,254
$1,323
$1,254
$1,323
$1,254
$1,323
$1,323
1120
928
320
0
640
0
384
944
1168
704
848
640
0
608
480
512
0
0
$2,372
$1,965
$678
$0
$1,355
$0
$813
$1,999
$2,473
$1,491
$1,796
$1,355
$0
$1,287
$1,016
$1,084
$0
$0
$1,575,573
$2,822,965
$1,339,920
$453,292
$17,636,896
$523,419
$2,462,431
$4,272,352
$202,324
$5,087,712
$1,656,428
$3,411,597
$184,085
$5,520,715
$11,506,875
$3,493,522
$1,653,883
$532,499
$64,336,487
7348
9332
3739
1510
45432
97952
Rotterdam
Antwerp
Copenhagen
Copenhagen
Le Havre
Hamburg
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,155
$1,096
$1,264
$1,264
$1,040
$1,254
736
160
0
816
208
240
$1,558
$339
$0
$1,728
$440
$508
$29,167,128
$25,107,883
$9,422,861
$6,413,173
$124,315,002
$295,638,029
East Europe
Armenia
Byelarus
Bulgaria
Com. of Indep. Sta
Croatia
Cyprus
Czechoslovakia
Estonia
Georgia
Hungary
Latvia
Lithuania
Macedonia
Poland
Romania
Slovenia
Yugoslavia
Ukraine
Subtotal
West Europe
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Luxembourg
Malta
Netherlands
Norway
Portugal
Spain
Sweden
Switzerland
UK
Subtotal
3374
145
3278
46843
691
218
1344
2452
7437
38634
3686
7139
125597
406150
Piraeus
Felixstowe
Felixstowe
La Spezid
Antwerp
Algeciras
Rotterdam
Copenhagen
Algeciras
Algeciras
Copenhagen
Rotterdam
Felixstowe
$1,256
$1,256
$1,2 5
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,323
$1,013
$1,013
$1,238
$1,096
$963
$1,155
$1,264
$963
$963
$1,264
$1,155
$1,013
0
1680
0
310
160
0
20
0
0
0
544
496
0
$0
$3,557
$0
$656
$339
$0
$42
$0
$0
$0
$1,152
$1,050
$0
$8,701,752
$843,328
$7,438,004
$147,551,564
$1,859,910
$482,574
$3,296,659
$6,179,052
$16,499,448
$85,713,113
$13,536,462
$24,710,757
$284,950,933
$1,091,827,630
31716
39
68
195
124
6341
39
6
5
35
Sydney Port
Sydney Port
Sydney Port
Sydney Port
Sydney Port
Auckland
Sydney Port
Auckland
Sydney Port
Auckland
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$5,222
$1,354
$1,354
$1,354
$1,354
$1,354
$1,177
$1,354
$1,177
$1,354
$1,177
0
0
0
0
0
0
0
0
0
0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$208,573,550
$258,023
$447,036
$1,283,819
$817,903
$40,575,261
$259,108
$35,475
$31,032
$225,099
$252,506,307
Pacific
Australia
Fiji
French Pacific Ocean
Guam
New Caledonia
New Zealand
Papua New Guinea
Samoa
Vanuatu
W. Samoa
tl
Qbt.,
L3UIJLULa
Total( for 1 year)
Total ( for 5 year)
3ODDY
1157718
5788590
$3,410,479,956
$17,052,399,781
Table A.2.2
i
I~1^
UUI
y
Iaes
orl
Taurus
_____
-
~
-
~
-
unit Cost from
Plant to Export Port
Africa
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gambia
Ghana
Guinea
Guinea-Bissau
Ivory Coast
Kenya
Liberia
Libya
Madagascar
Malawi
-
import ror
Distribution Cost in Global System with 2 Plants
Plant Location : Detroit, U.S. & Paris, France
Supply Plant : Paris
Paris to Le Havre
428
121
176
55
50
61
134
13
18
2
12
17
12
2
13
13
20
127
8
21
504
129
5
857
4
43
Casablanca
Durban
Abidjan
Durban
Abidjan
Durban
Abidjan
Durban
Abidjan
Abidjan
Durban
Durban
Mombasa
Durban
Mombasa
Mombasa
Durban
Abidjan
Abidjan
Durban
Abidjan
Mombasa
Abidjan
Casablanca
Durban
Durban
I
Unit Cost from
ImportPort to Capital
--
Export to Import Ports
I Distance
Total Cost
I Unit Cost
Le Havre to Import Ports
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$338
$2,237
$1,190
$2,237
$1,190
$2,237
$1,190
$2,237
$1,190
$1,190
$2,237
$2,237
$2,800
$2,237
$2,800
$2,800
$2,237
$1,190
$1,190
$2,237
$1,190
$2,800
$1,190
$338
$2,237
$2,237
560
1488
352
432
400
1472
848
1120
1200
1120
1280
1664
1040
800
800
688
1040
192
800
800
0
0
352
1104
0
928
$1,186
$3,151
$745
$915
$847
$3,117
$1,796
$2,372
$2,541
$2,372
$2,710
$3,524
$2,202
$1,694
$1,694
$1,457
$2,202
$407
$1,694
$1,694
$0
$0
$745
$2,338
$0
$1,965
$739,867
$675,062
$376,754
$184,564
$111,437
$337,236
$425,872
$64,159
$69,395
$8,696
$64,351
$99,557
$60,671
$8,334
$61,653
$58,540
$92,607
$229,009
$23,662
$86,374
$702,062
$387,887
$9,655
$2,466,698
$9,940
$189,626
Mali
Mauritania
Mauritius
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Saint Lucia
Sao Tome & Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Tunisia
Uganda
Zaire
Zambia
Zimbabwe
Subtotal
23
40
63
1991
40
12
110
953
23
16
5
47
15
47
0
4161
630
42
174
68
67
48
210
252
1525
13405
Casablanca
Casablanca
Durban
Casablanca
Durban
Durban
Abidjan
Abidjan
Durban
Durban
Durban
Abidjan
Durban
Durban
Mombasa
Durban
Mombasa
Durban
Mombasa
Abidjan
Casablanca
Mombasa
Durban
Durban
Durban
Caribbean America
Bahamas
Barbados
Bermuda
Cuba
Dominican Republic
Guadeloupe
Haiti
35
28
30
10
216
252
7
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
Supply Plant : Detroit
Detroit to N.Y.
N.Y. to San Juan
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$338
$338
$2,237
$338
$2,237
$2,237
$1,190
$1,190
$2,237
$2,237
$2,237
$1,190
$2,237
$2,237
$2,800
$2,237
$2,800
$2,237
$2,800
$1,190
$338
$2,800
$2,237
$2,237
$2,237
$455
$455
$455
$455
$455
$455
$455
1232
1040
0
0
224
848
576
352
800
800
800
832
1120
1120
448
0
1088
0
192
288
896
448
1632
912
640
0
0
0
0
0
0
0
$2,609
$2,202
$0
$0
$474
$1,796
$1,220
$745
$1,694
$1,694
$1,694
$1,762
$2,372
$2,372
$949
$0
$2,304
$0
$407
$610
$1,897
$949
$3,456
$1,931
$1,355
$0
$0
$0
$0
$0
$0
$0
$70,979
$108,887
$152,628
$1,077,521
$115,353
$52,921
$286,948
$2,038,737
$95,489
$65,020
$20,400
$149,790
$70,727
$225,315
$0
$10,152,490
$3,343,546
$103,494
$592,920
$135,725
$162,845
$190,053
$1,238,054
$1,101,466
$5,785,762
$34,880,733
$59,454
$47,081
$50,931
$16,327
$369,252
$431,273
$12,168
Jamaica
Martinique
Netherlands Antilles
Puerto Rico
Trinidad & Tobago
Virgin Islands
Other
Subtotal
114
San Juan
$1,256
$455
0
$0
0
San Juan
$1,256
$455
0
$0
$0
194
1806
108
15
363
3175
San Juan
San Juan
San Juan
San Juan
San Juan
$1,256
$1,256
$1,256
$1,256
$1,256
$455
$455
$455
$455
$455
0
0
0
0
0
$0
$0
$0
$0
$0
$331,156
$3,090,326
$184,164
$26,492
$620,417
$5,434,140
$1,400
$666
$1,111
$1,636
$1,542
$666
$666
$1,111
$666
$666
$666
$666
$666
$666
$666
$1,636
$1,111
$666
$1,636
$666
0
672
512
0
0
352
0
560
960
1424
480
1120
320
208
0
624
0
1280
832
640
$0
$1,423
$1,084
$0
$0
$745
$0
$1,186
$2,033
$3,015
$1,016
$2,372
$678
$440
$0
$1,321
$0
$2,710
$1,762
$1,355
$24,890,438
$1,505
$310,644
$102,438,610
$5,344,674
$3,233,246
$1,078,208
$4,050,691
$562,245
$344,818
$767,700
$61,827
$70,889
$425,937
$1,109,574
$736,623
$781,894
$1,459,185
$2,916,809
$5,504,903
$156,090,422
C. & S. America
Argentina
Belize
Bolivia
Brazil
Chile
Colombia
Costa Rica
Ecuador
El Salvador
French Guiana
Guatemala
Guyana
Honduras
Nicaragua
Panama
Paraguay
Peru
Suriname
Uruguay
Venezuela
Subtotal
9371
0
90
35424
1910
1212
561
1140
142
70
261
14
27
180
577
175
330
315
627
1680
54108
Buenos Aires
Cristobal
Callao
Santo's
Valparaso
Cristobal
Limon-Moin
Callao
Cristobal
Cristobal
Limon-Moin
Cristobal
Limon-Moin
Limon-Moin
Cristobal
Santo's
Callao
Cristobal
Santo's
Cristobal
Supply Plant: Detroit
Detroit to N.Y.
N.Y. to Import Ports
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$195,100
North America
Canada
Mexico
United States
Subtotal
Supply Plant : Detroit
1
382691
8661
498405
545335
Far East
$0
$0
$0
$0
$0
$0
QS11
u
tota
1P
$128,442,845
$56,327,119
$1,040,647,567
$1,225,417,530
Supply Plant : Paris
Paris to Le Havre
Afghanistan
Bangladesh
Brunei
Burma
Cambodia
China
Hong Kong
India
Indonesia
Japan
Laos
S. Korea
Malaysia
Maldives
Mongolia
Pakistan
Philippines
Singapore
Sri Lanka
Taiwan
Thailand
Vietnam
$3,356
$6,503
1
19
52
0
134
6314
1099
4350
98
23404
306
3421
600
0
84
1465
2681
1565
381
19986
2340
19
6O3L1
Singapore
Singapore
Singapore
Singapore
Singapore
Shanghai
Hong Kong
Madras
Singapore
Yokohama
Singapore
Pusan
Singapore
Singapore
Shanghai
Madras
Marila
Singapore
Madras
Kaoshung
Singapore
Singapore
Le Havre to Import Ports
$203
$203
$203
$203
$203
$3,848
$3,848
$3,848
$3,848
$3,848
0
0
0
0
0
$0
$0
$0
$0
$0
$4,709
$78,389
$209,950
$887
$544,812
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$4,576
$4,328
$3,148
$3,848
592
0
560
0
$1,254
$0
$1,186
$0
$38,092,212
$4,979,605
$19,735,940
$398,587
$4,802
$3,848
$4,802
$3,848
$3,848
$4,576
$3,148
$4,287
$3,848
$3,148
$4,287
$3,848
0
0
160
0
0
1344
1280
0
0
0
$0
$0
$339
$0
$0
$2,846
$2,710
$0
$0
$0
$117,125,073
$1,238,674
$18,280,480
$2,429,463
$1,896
$642,360
$8,879,994
$12,037,495
$6,340,698
$1,278,114
0
80
$0
$169
$89,739,087
$9,876,039
$3,848
0
$0
$74,986
$ 74•1
$331,989,451
Mid East
Subtotal
Supply Plant : Paris
Paris to Le Havre
11555
East Europe
Armenia
Byelarus
Bulgaria
Com. of Indep. Sta
Croatia
Cyprus
Czechoslovakia
Estonia
Georgia
Hungary
Latvia
Lithuania
Macedonia
Poland
Romania
Slovenia
Yugoslavia
Ukraine
Subtotal
318
631
411
176
4563
188
741
948
40
1272
385
883
71
1454
3200
972
641
206
17100
560
$1,186
$57,683,543
By Land Directly
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
2320
1424
1216
900
736
1920
656
$4,913
$3,015
$2,575
$1,906
$1,558
$4,066
$1,389
$1,563,383
$1,902,136
$1,059,329
$334,920
$7,111,331
$765,745
$1,029,306
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
1520
2304
880
1376
1328
1120
$3,219
$4,879
$1,863
$2,914
$2,812
$2,372
$3,049,727
$195,363
$2,369,681
$1,120,924
$2,482,028
$169,261
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
1024
1328
832
1008
1488
$2,168
$2,812
$1,762
$2,134
$3,151
$3,152,298
$8,999,012
$1,712,427
$1,368,630
$650,493
$39,035,996
$0
$0
$0
$0
$0
$0
752
208
768
1600
0
752
$1,592
$440
$1,626
$3,388
$0
$1,592
$11,700,403
$4,110,354
$6,080,320
$5,114,699
$0
$155,974,234
Supply Plant : Paris
7348
9332
3739
1510
45432
97952
$3,603
$203)
(
Supply Plant : Paris
West Europe
Austria
Belgium
Denmark
Finland
France
Germany
Jeddah
By Land Directly
$0
$0
$0
$0
$0
$0
Greece
Iceland
Ireland
Italy
Luxembourg
Malta
Netherlands
Norway
Portugal
Spain
Sweden
Switzerland
UK
Subtotal
3374
145
3278
46843
691
218
1344
2452
7437
38634
3686
7139
125597
406150
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Pacific
Australia
Fiji
French Pacific Ocean
Guam
New Caledonia
New Zealand
Papua New Guinea
Samoa
Vanuatu
W. Samoa
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
1376
1952
624
752
224
1152
368
1120
960
768
1280
288
272
$2,914
$4,133
$1,321
$1,592
$474
$2,439
$779
$2,372
$2,033
$1,626
$2,710
$610
$576
$9,829,827
$598,296
$4,331,847
$74,590,619
$327,903
$530,589
$1,047,092
$5,814,633
$15,117,581
$62,827,550
$9,991,198
$4,353,762
$72,338,769
$444,679,675
0
0
0
0
0
0
0
0
0
0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$135,037,530
$167,060
$289,439
$831,224
$529,561
$29,580,368
$167,762
$25,862
$20,092
$164,103
$166,813,002
Supply Plant : Paris
Paris to Le Havre
31716
39
68
195
124
6341
39
6
5
35
Subtotal
385•Y
Total ( for 1 year )
Total ( for 5 year )
1157718
5788590
Sydney Port
Sydney Port
Sydney Port
Sydney Port
Sydney Port
Auckland
Sydney Port
Auckland
Sydney Port
Auckland
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$4,055
$4,055
$4,055
$4,055
$4,055
$4,462
$4,055
$4,462
$4,055
$4,462
$2,462,024,493
$12,310,122,465
Table A.2.3
Distribution Cost in Global System with 3 Plants
Plant Location : Detroit, U.S. & Paris, France
Taiwan
Country
Sales for
Import Port
Taurus
S
Africa
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gambia
Ghana
Guinea
Guinea-Bissau
Ivory Coast
Kenya
Liberia
Libya
Madagascar
Malawi
Mali
Unit Cost from
Unit Cost from
Plant to Export Port
Export to Import Ports
Supply Plant : Paris
Paris to Le Havre
428
121
176
55
50
61
134
13
18
2
12
17
12
2
13
13
20
127
8
21
504
129
5
857
4
43
23
Casablanca
Durban
Abidjan
Durban
Abidjan
Durban
Abidjan
Durban
Abidjan
Abidjan
Durban
Durban
Mombasa
Durban
Mombasa
Mombasa
Durban
Abidjan
Abidjan
Durban
Abidjan
Mombasa
Abidjan
Casablanca
Durban
Durban
Casablanca
Import Port to Capital
Distance
Total Cost
Unit Cost
Le Havre to Import Ports
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$338
$2,237
$1,190
$2,237
$1,190
$2,237
$1,190
$2,237
$1,190
$1,190
$2,237
$2,237
$2,800
$2,237
$2,800
$2,800
$2,237
$1,190
$1,190
$2,237
$1,190
$2,800
$1,190
$338
$2,237
$2,237
$338
560
1488
352
432
400
1472
848
1120
1200
1120
1280
1664
1040
800
800
688
1040
192
800
800
0
0
352
1104
0
928
1232
$1,186
$3,151
$745
$915
$847
$3,117
$1,796
$2,372
$2,541
$2,372
$2,710
$3,524
$2,202
$1,694
$1,694
$1,457
$2,202
$407
$1,694
$1,694
$0
$0
$745
$2,338
$0
$1,965
$2,609
$739,867
$675,062
$376,754
$184,564
$111,437
$337,236
$425,872
$64,159
$69,395
$8,696
$64,351
$99,557
$60,671
$8,334
$61,653
$58,540
$92,607
$229,009
$23,662
$86,374
$702,062
$387,887
$9,655
$2,466,698
$9,940
$189,626
$70,979
Mauritania
Mauritius
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Saint Lucia
Sao Tome & Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Tunisia
Uganda
Zaire
Zambia
Zimbabwe
Subtotal
40
Casablanca
$203
$338
1040
$2,202
63
1991
40
12
110
953
23
16
5
47
15
47
0
4161
630
42
174
68
67
48
210
252
1525
13405
$108,887
Durban
Casablanca
Durban
Durban
Abidjan
Abidjan
Durban
Durban
Durban
Abidjan
Durban
Durban
Mombasa
Durban
Mombasa
Durban
Mombasa
Abidjan
Casablanca
Mombasa
Durban
Durban
Durban
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$2,237
$338
$2,237
$2,237
$1,190
$1,190
$2,237
$2,237
$2,237
$1,190
$2,237
$2,237
$2,800
$2,237
$2,800
$2,237
$2,800
$1,190
$338
$2,800
$2,237
$2,237
$2,237
0
0
224
848
576
352
800
800
800
832
1120
1120
448
0
1088
0
192
288
896
448
1632
912
640
$0
$0
$474
$1,796
$1,220
$745
$1,694
$1,694
$1,694
$1,762
$2,372
$2,372
$949
$0
$2,304
$0
$407
$610
$1,897
$949
$3,456
$1,931
$1,355
$152,628
$1,077,521
$115,353
$52,921
$286,948
$2,038,737
$95,489
$65,020
$20,400
$149,790
$70,727
$225,315
$0
$10,152,490
$3,343,546
$103,494
$592,920
$135,725
$162,845
$190,053
$1,238,054
$1,101,466
$5,785,762
$34,880,733
$455
$455
$455
$455
$455
$455
$455
$455
0
0
0
0
0
0
0
0
$0
$0
$0
$0
$0
$0
$0
$0
Caribbean America
Bahamas
Barbados
Bermuda
Cuba
Dominican Republic
Guadeloupe
Haiti
Jamaica
35
28
30
10
216
252
7
114
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
Supply Plant : Detroit
Detroit to N.Y.
N.Y. to San Juan
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$59,454
$47,081
$50,931
$16,327
$369,252
$431,273
$12,168
$195,100
Martinique
Netherlands Antilles
Puerto Rico
Trinidad & Tobago
Virgin Islands
Other
Subtotal
0
194
1806
108
San
San
San
San
Juan
Juan
Juan
Juan
$1,256
$1,256
$1,256
$1,256
$455
$455
$455
$455
0
0
0
0
$0
$0
$0
$0
15
San Juan
$1,256
$455
363
3175
0
San Juan
$0
$26,492
$1,256
$455
0
$0
$620,417
$5,434,140
0
672
512
0
0
352
0
560
960
1424
480
1120
320
208
0
624
0
1280
832
640
$0
$1,423
$1,084
$0
$0
$745
$0
$1,186
$2,033
$3,015
$1,016
$2,372
$678
$440
$0
$1,321
$0
$2,710
$1,762
$1,355
$0
$1,506
$276,519
$102,778,040
$4,098,899
$3,235,743
$1,061,861
$3,618,440
$562,538
$344,962
$760,086
$61,857
$70,095
$420,683
$1,110,764
$738,299
$656,667
$1,459,834
$2,922,815
$5,508,363
$129,687,968
$0
$331,156
$3,090,326
$184,164
1948.96
C. & S. America
Supply Plant : Taiwan
Taiwan to Import Ports
Argentina
Belize
Bolivia
Brazil
Chile
Colombia
Costa Rica
Ecuador
El Salvador
French Guiana
Guatemala
Guyana
Honduras
Nicaragua
Panama
Paraguay
Peru
Suriname
Uruguay
Venezuela
QI-
ULU
tLal
9371
0
90
35424
1910
1212
561
1140
142
70
261
14
27
180
577
175
330
315
627
1680
5410U
Buenos Aires
Cristobal
Callao
Santo's
Valparaso
Cristobal
Limon-Moin
Callao
Cristobal
Cristobal
Limon-Moin
Cristobal
Limon-Moin
Limon-Moin
Cristobal
Santo's
Callao
Cristobal
Santo's
Cristobal
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$1,924
$1,988
$2,901
$2,146
$1,924
$1,893
$1,988
$1,924
$1,924
$1,893
$1,924
$1,893
$1,893
$1,924
$2,901
$1,988
$1,924
$2,901
$1,924
North America
Supply Plant : Detroit
Canada
Mexico
38269
8661
$0
$0
$0
$0
United States
498405
$0
$0
Subtotal
545335
1
·
$3,356
$6,503
·
$128,442,845
$56,327,119
$1,040,647,567
$1,225,417,530
Far East
Supply Plant : Taiwan
Taiwan to Import Ports
Afghanistan
Bangladesh
Brunei
Burma
Cambodia
China
Hong Kong
India
Indonesia
Japan
Laos
S. Korea
Malaysia
Maldives
Mongolia
Pakistan
Philippines
Singapore
Sri Lanka
Taiwan
Thailand
Vietnam
Subtotal
1
19
52
0
134
6314
1099
4350
98
23404
306
3421
600
0
84
1465
2681
1565
381
19986
2340
19
68321
Singapore
Singapore
Singapore
Singapore
Singapore
Shanghai
Hong Kong
Madras
Singapore
Yokohama
Singapore
Pusan
Singapore
Singapore
Shanghai
Madras
Marila
Singapore
Madras
Kaoshung
Singapore
Singapore
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$374
$374
$374
$374
$374
$127
$92
$704
$374
$325
$374
$325
$374
$374
$127
$704
$19
$374
$704
$0
$374
$374
0
0
0
0
0
592
0
560
0
0
0
160
0
0
1344
1280
0
0
0
0
80
0
$0
$0
$0
$0
$0
$1,254
$0
$1,186
$0
$0
$0
$339
$0
$0
$2,846
$2,710
$0
$0
$0
$0
$169
$0
$435
$7,245
$19,403
$82
$50,351
$8,718,679
$100,582
$8,219,159
$36,837
$7,608,718
$114,477
$2,271,344
$224,528
$175
$250,463
$5,001,460
$50,187
$585,999
$268,370
$0
$1,272,491
$6,930
$34,807,915
·
Mid East
Subtotal
Supply Plant : Taiwan
Taiwan to Import Ports
11555
East Europe
Jeddah
$1,310
Supply Plant : Paris
560
$1,186
$28,843,665
By Land Directly
Armenia
Byelarus
Bulgaria
Com. of Indep. Sta
Croatia
Cyprus
Czechoslovakia
Estonia
Georgia
Hungary
318
631
411
176
4563
188
741
948
40
1272
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
2320
1424
1216
900
736
1920
656
1520
2304
880
$4,913
$3,015
$2,575
$1,906
$1,558
$4,066
$1,389
$3,219
$4,879
$1,863
$1,563,383
$1,902,136
$1,059,329
$334,920
$7,111,331
$765,745
$1,029,306
$3,049,727
$195,363
$2,369,681
Latvia
Lithuania
Macedonia
Poland
Romania
385
883
71
1454
3200
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
1376
1328
1120
1024
1328
$2,914
$2,812
$2,372
$2,168
$2,812
$1,120,924
$2,482,028
$169,261
$3,152,298
$8,999,012
Slovenia
Yugoslavia
972
641
$0
$0
$0
$0
832
1008
$1,762
$2,134
$1,712,427
$1,368,630
206
17100
$0
$0
1488
$3,151
$650,493
$39,035,996
$0
$0
$0
$0
$0
$0
752
208
768
1600
0
752
$1,592
$440
$1,626
$3,388
$0
$1,592
$11,700,403
$4,110,354
$6,080,320
$5,114,699
$0
$155,974,234
Ukraine
Subtotal
West Europe
Austria
Belgium
Denmark
Finland
France
Germany
Supply Plant : Paris
7348
9332
3739
1510
45432
97952
By Land Directly
$0
$0
$0
$0
$0
$0
Greece
Iceland
Ireland
Italy
Luxembourg
Malta
Netherlands
Norway
Portugal
Spain
Sweden
Switzerland
UK
Subtotal
3374
145
3278
46843
691
218
1344
2452
7437
38634
3686
7139
125597
406150
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Pacific
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
1376
1952
624
752
224
1152
368
1120
960
768
1280
288
272
$2,914
$4,133
$1,321
$1,592
$474
$2,439
$779
$2,372
$2,033
$1,626
$2,710
$610
$576
$9,829,827
$598,296
$4,331,847
$74,590,619
$327,903
$530,589
$1,047,092
$5,814,633
$15,117,581
$62,827,550
$9,991,198
$4,353,762
$72,338,769
$444,679,675
0
0
0
0
0
0
0
0
0
0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$43,309,467
$53,580
$92,829
$266,592
$169,842
$11,241,089
$53,805
$9,828
$6,444
$62,362
$55,265,837
Supply Plant : Taiwan
Taiwan to Import Ports
Australia
Fiji
French Pacific Ocean
Guam
New Caledonia
New Zealand
Papua New Guinea
Samoa
Vanuatu
W. Samoa
S
kUUbt
cl
oLIall
Total ( for 1 year )
Total ( for 5 year )
31716
39
68
195
124
6341
39
6
5
35
300Y0
1157718
5788590
Sydney Port
Sydney Port
Sydney Port
Sydney Port
Sydney Port
Auckland
Sydney Port
Auckland
Sydney Port
Auckland
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$1,366
$1,366
$1,366
$1,366
$1,366
$1,773
$1,366
$1,773
$1,366
$1,773
$1,998,053,461
$9,990,267,306
Table A.2.4
Country
mI
Sales for
Import Port
Taurus
Africa
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gambia
Ghana
Guinea
Guinea-Bissau
Ivory Coast
Kenya
Liberia
Libya
Madagascar
Malawi
Distribution Cost in Global System with 3 Plants
Plant Location : Detroit, & Salt Lake City, U.S.
Paris, France
Unit Cost from
Unit Cost from
Plant to Export Port
Export to Import Ports
Supply Plant : Paris
Paris to Le Havre
428
121
176
55
50
61
134
13
18
2
12
17
12
2
13
13
20
127
8
21
504
129
5
857
4
43
Casablanca
Durban
Abidjan
Durban
Abidjan
Durban
Abidjan
Durban
Abidjan
Abidjan
Durban
Durban
Mombasa
Durban
Mombasa
Mombasa
Durban
Abidjan
Abidjan
Durban
Abidjan
Mombasa
Abidjan
Casablanca
Durban
Durban
Import Port to Capital
Total Cost
Distance m Unit Cost
Le Havre to Import Ports
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$338
$2,237
$1,190
$2,237
$1,190
$2,237
$1,190
$2,237
$1,190
$1,190
$2,237
$2,237
$2,800
$2,237
$2,800
$2,800
$2,237
$1,190
$1,190
$2,237
$1,190
$2,800
$1,190
$338
$2,237
$2,237
560
1488
352
432
400
1472
848
1120
1200
1120
1280
1664
1040
800
800
688
1040
192
800
800
0
0
352
1104
0
928
$1,186
$3,151
$745
$915
$847
$3,117
$1,796
$2,372
$2,541
$2,372
$2,710
$3,524
$2,202
$1,694
$1,694
$1,457
$2,202
$407
$1,694
$1,694
$0
$0
$745
$2,338
$0
$1,965
$739,867
$675,062
$376,754
$184,564
$111,437
$337,236
$425,872
$64,159
$69,395
$8,696
$64,351
$99,557
$60,671
$8,334
$61,653
$58,540
$92,607
$229,009
$23,662
$86,374
$702,062
$387,887
$9,655
$2,466,698
$9,940
$189,626
Mali
Mauritania
Mauritius
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Saint Lucia
Sao Tome & Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Tunisia
Uganda
Zaire
Zambia
Zimbabwe
Subtotal
23
40
63
1991
40
12
110
953
23
16
5
47
15
47
0
4161
630
42
174
68
67
48
210
252
1525
13405
Casablanca
Casablanca
Durban
Casablanca
Durban
Durban
Abidjan
Abidjan
Durban
Durban
Durban
Abidjan
Durban
Durban
Mombasa
Durban
Mombasa
Durban
Mombasa
Abidjan
Casablanca
Mombasa
Durban
Durban
Durban
Caribbean America
Bahamas
Barbados
Bermuda
Cuba
Dominican Republic
Guadeloupe
Haiti
35
28
30
10
216
252
7
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
San Juan
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
$203
Supply Plant : Detroit
Detroit to N.Y.
N.Y. to San Juan
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$338
$338
$2,237
$338
$2,237
$2,237
$1,190
$1,190
$2,237
$2,237
$2,237
$1,190
$2,237
$2,237
$2,800
$2,237
$2,800
$2,237
$2,800
$1,190
$338
$2,800
$2,237
$2,237
$2,237
$455
$455
$455
$455
$455
$455
$455
1232
1040
0
0
224
848
576
352
800
800
800
832
1120
1120
448
0
1088
0
192
288
896
448
1632
912
640
$2,609
$2,202
$0
$0
$474
$1,796
$1,220
$745
$1,694
$1,694
$1,694
$1,762
$2,372
$2,372
$949
$0
$2,304
$0
$407
$610
$1,897
$949
$3,456
$1,931
$1,355
$70,979
$108,887
$152,628
$1,077,521
$115,353
$52,921
$286,948
$2,038,737
$95,489
$65,020
$20,400
$149,790
$70,727
$225,315
$0
$10,152,490
$3,343,546
$103,494
$592,920
$135,725
$162,845
$190,053
$1,238,054
$1,101,466
$5,785,762
$34,880,733
0
0
0
0
0
0
0
$0
$0
$0
$0
$0
$0
$0
$59,454
$47,081
$50,931
$16,327
$369,252
$431,273
$12,168
Jamaica
Martinique
Netherlands Antilles
Puerto Rico
Trinidad & Tobago
Virgin Islands
Other
Subtotal
114
0
194
1806
108
15
363
3175
San
San
San
San
San
San
San
Juan
Juan
Juan
Juan
Juan
Juan
Juan
C. & S. America
Argentina
Belize
Bolivia
Brazil
Chile
Colombia
Costa Rica
Ecuador
El Salvador
French Guiana
Guatemala
Guyana
Honduras
Nicaragua
Panama
Paraguay
Peru
Suriname
Uruguay
Venezuela
Subtotal
9371
0
90
35424
1910
1212
561
1140
142
70
261
14
27
180
577
175
330
315
627
1680
54108
Buenos Aires
Cristobal
Callao
Santo's
Valparaso
Cristobal
Limon-Moin
Callao
Cristobal
Cristobal
Limon-Moin
Cristobal
Limon-Moin
Limon-Moin
Cristobal
Santo's
Callao
Cristobal
Santo's
Cristobal
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
Supply Plant : Detroit
Detroit to N.Y.
N.Y. to Import Ports
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$1,256
$455
$455
$455
$455
$455
$455
$455
0
0
0
0
0
0
0
$0
$0
$0
$0
$0
$0
$0
$195,100
$0
$331,156
$3,090,326
$184,164
$26,492
$620,417
$5,434,140
$1,400
$666
$1,111
$1,636
$1,542
$666
$666
$1,111
$666
$666
$666
$666
$666
$666
$666
$1,636
$1,111
$666
$1,636
$666
0
672
512
0
0
352
0
560
960
1424
480
1120
320
208
0
624
0
1280
832
640
$0
$1,423
$1,084
$0
$0
$745
$0
$1,186
$2,033
$3,015
$1,016
$2,372
$678
$440
$0
$1,321
$0
$2,710
$1,762
$1,355
$24,890,438
$1,505
$310,644
$102,438,610
$5,344,674
$3,233,246
$1,078,208
$4,050,691
$562,245
$344,818
$767,700
$61,827
$70,889
$425,937
$1,109,574
$736,623
$781,894
$1,459,185
$2,916,809
$5,504,903
$156,090,422
North America
Canada
Mexico
United States
Subtotal
I
Supply Plant : Detroit and Salt Lake City
by land directly
$0
$0
$0
38269
8661
498405
545335
Far East
$0
$0
$0
$1,804
$6,503
$69,019,322
$56,324,434
$684,329,239
$809,672,995
$0
$0
$0
$0
$0
$1,254
$0
$1,186
$0
$0
$0
$339
$0
$0
$2,846
$2,710
$0
$0
$0
$0
$169
$0
$3,777
$62,867
$168,376
$712
$436,930
$25,727,171
$3,230,723
$20,723,390
$319,660
$61,297,570
$993,395
$10,397,125
$1,948,387
$1,521
$477,388
$9,212,540
$7,964,640
$5,085,130
$1,364,690
$59,376,100
$7,998,906
$60,138
$216,851,134
Supply Plant : Salt Lake City
Afghanistan
Bangladesh
Brunei
Burma
Cambodia
China
Hong Kong
India
Indonesia
Japan
Laos
S. Korea
Malaysia
Maldives
Mongolia
Pakistan
Philippines
Singapore
Sri Lanka
Taiwan
Thailand
Vietnam
1
19
52
0
134
6314
1099
4350
98
23404
306
3421
600
0
84
1465
2681
1565
381
19986
2340
19
LSubtotal
68321
Singapore
Singapore
Singapore
Singapore
Singapore
Shanghai
Hong Kong
Madras
Singapore
Yokohama
Singapore
Pusan
Singapore
Singapore
Shanghai
Madras
Marila
Singapore
Madras
Kaoshung
Singapore
Singapore
Salt Lake City to L.A.
L.A. to Import Ports
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,636
$1,636
$1,636
$1,636
$1,636
$1,208
$1,327
$1,966
$1,636
$1,007
$1,636
$1,088
$1,636
$1,636
$1,208
$1,966
$1,358
$1,636
$1,966
$1,358
$1,636
$1,636
0
0
0
0
0
592
0
560
0
0
0
160
0
0
1344
1280
0
0
0
0
80
0
I
Mid East
Subtotal
I
Supply Plant : Paris
Paris to Le Havre
11555
East Europe
Armenia
Byelarus
Bulgaria
Com. of Indep. Sta
Croatia
Cyprus
Czechoslovakia
Estonia
Georgia
Hungary
Latvia
Lithuania
Macedonia
Poland
Romania
Slovenia
Yugoslavia
Ukraine
Subtotal
$203
Supply Plant : Paris
318
631
411
176
4563
188
741
948
40
1272
385
883
71
1454
3200
972
641
206
17100
West Europe
Austria
Belgium
Denmark
Finland
France
Germany
Jeddah
560
$1,186
$57,683,543
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
2320
1424
1216
900
736
1920
656
1520
2304
880
1376
1328
1120
1024
1328
832
1008
1488
$4,913
$3,015
$2,575
$1,906
$1,558
$4,066
$1,389
$3,219
$4,879
$1,863
$2,914
$2,812
$2,372
$2,168
$2,812
$1,762
$2,134
$3,151
$1,563,383
$1,902,136
$1,059,329
$334,920
$7,111,331
$765,745
$1,029,306
$3,049,727
$195,363
$2,369,681
$1,120,924
$2,482,028
$169,261
$3,152,298
$8,999,012
$1,712,427
$1,368,630
$650,493
$39,035,996
$0
$0
$0
$0
$0
$0
752
208
768
1600
0
752
$1,592
$440
$1,626
$3,388
$0
$1,592
$11,700,403
$4,110,354
$6,080,320
$5,114,699
$0
$155,974,234
By Land Directly
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Supply Plant : Paris
7348
9332
3739
1510
45432
97952
$3,603
By Land Directly
$0
$0
$0
$0
$0
$0
Greece
Iceland
Ireland
Italy
Luxembourg
Malta
Netherlands
Norway
Portugal
Spain
Sweden
Switzerland
UK
Subtotal
3374
145
3278
46843
691
218
1344
2452
7437
38634
3686
7139
125597
406150
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Pacific
Australia
Fiji
French Pacific Ocean
Guam
New Caledonia
New Zealand
Papua New Guinea
Samoa
Vanuatu
W. Samoa
Subtotal
Total ( for 1 year)
Total ( for 5 year )
31716
39
68
195
124
6341
39
6
5
35
38569
1157718
5788590
Sydney Port
Sydney Port
Sydney Port
Sydney Port
Sydney Port
Auckland
Sydney Port
Auckland
Sydney Port
Auckland
Supply Plant : Salt Lake City
Salt Lake City to L.A.
L.A. to Import Ports
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$1,613
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$1,354
$1,354
$1,354
$1,354
$1,354
$1,177
$1,354
$1,177
$1,354
$1,177
1376
1952
624
752
224
1152
368
1120
960
768
1280
288
272
$2,914
$4,133
$1,321
$1,592
$474
$2,439
$779
$2,372
$2,033
$1,626
$2,710
$610
$576
$9,829,827
$598,296
$4,331,847
$74,590,619
$327,903
$530,589
$1,047,092
$5,814,633
$15,117,581
$62,827,550
$9,991,198
$4,353,762
$72,338,769
$444,679,675
0
0
0
0
0
0
0
0
0
0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$94,097,821
$116,412
$201,689
$579,220
$369,013
$17,688,019
$116,901
$15,465
$14,001
$98,128
$113,296,668
$1,877,625,306
$9,388,126,528